Real Time Artificial Lift Timing and Selection Using Hybrid Data-Driven and Physics Models

Abstract

A method of forecasting production using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, comprising: receiving sensor feedback from the well during well production; receiving a flowrate of oil, gas, and water for the well; calculating average reservoir pressure and productivity index (PI) of the well based on a cumulative liquid rate; estimating reservoir deliverability based on the PI and average reservoir pressure; estimating well deliverability, based on one or more artificial lift parameters; estimating a bottomhole pressure (BHP) of the well based, at least in part, on the estimated reservoir deliverability and well deliverability; generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback; and producing fluids from the well based on the estimated liquid, gas, water, and oil production of the well.

Description

TECHNICAL FIELD

This disclosure relates to planning artificial lift operations and, more particularly, to methods for selecting the type, timing, and design parameters of artificial lift operations for use in one or more wells using hybrid data-driven and physics-based models.

BACKGROUND

Numerous technology advancements have been developed in the oil and gas industry to optimize production throughout the life cycle of a well. Artificial lift is a method to lower a producing bottomhole pressure (BHP) on the formation and add energy to a fluid to increase the flow of the fluid in order to obtain a production rate of the fluid from a well. The fluid may be crude oil, water, gas, or a combination thereof. Artificial lift is often used in unconventional wells, such as shale, tight, or coalbed methane, that have low permeability and require hydraulic fracturing to stimulate production. An artificial lift system may include one or more positive-displacement downhole mechanical devices, such as a beam pump, a jet pump, a progressive cavity pump (PCP), a hydraulic pump, a gas lift, a sucker rod pump (SRP), and an electric submersible pump (ESP), in the well to pump the fluid to the surface. The artificial lift pumping system may implement a surface power source to drive a downhole pump assembly. The artificial lift pumping system may decrease the weight of a hydrostatic column by injecting gas into the liquid some distance down the well.

Multistage fracturing in unconventional wells increases contacted reservoir surface area and improves overall reservoir deliverability to produce economical rates in low permeability reservoirs. However, unconventional reservoirs also show quick decline rates from these fractured horizontal wells as the pressure depletes and fracture conductivities degrade. Compared to high permeability reservoirs, unconventional production is highly dynamic. Traditional methods for planning, selecting, and designing artificial lift applications may be ineffective, if not outright unsuccessful, in unconventional wells without accounting for this transient performance. With a careful artificial lift timing and selection strategy, there is significant potential to improve the efficiency of artificial lift operations in unconventional wells and to optimize field production.

Existing techniques to lift oil and gas can evaluate field development to improve operation, performance, longevity, etc. However, lifting oil and gas in an optimal manner and economically is one of the most challenging phases of field development with depleting reservoir energy. Traditional approaches to lift selection are not sufficient to manage unconventional wells effectively, which have initially high decline rates. It is of interest to understand production behavior in hydrocarbon wells under different lift conditions since the decision on artificial types, timing, and design parameters for the artificial lift system is helpful for optimizing well performance. Thus, the artificial lift system may be applied dynamically throughout the production of unconventional wells to maximize the value of unconventional oil and gas assets.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings illustrate certain aspects of some of the embodiments of the present disclosure and should not be used to limit or define the claims.

FIG. 1 illustrates a schematic representation of applications of a transient well performance (TWP) system, in accordance with certain embodiments.

FIG. 2 illustrates a schematic representation of a well performance analysis workflow, in accordance with certain embodiments.

FIG. 3 illustrates a schematic representation of material balance applied to a succession of pseudo-steady-state conditions with expanding drainage volume, in accordance with certain embodiments.

FIGS. 4A and 4B illustrate a set of plots of oil productivity index for several wells from a liquid-rich shale play, in accordance with certain embodiments.

FIG. 5 illustrates a set of plots showing the dynamic inflow performance relationship (IPR) of a well, in accordance with certain embodiments.

FIG. 6 illustrates a flow chart of an example method for performing productivity index (PI) based forecasting (PIBF) of liquid, oil, water, and gas rates, in accordance with certain embodiments.

FIGS. 7A-7D illustrate plots of PIBF of liquid rates and oil rates for a liquid well, in accordance with certain embodiments.

FIGS. 8A-8D illustrate plots of PIBF of gas rates for two different wells, in accordance with certain embodiments.

FIG. 9 illustrates a plot of an IPR curve with a realistic PI alongside an IPR curve represented using a simplified straight line method, in accordance with certain embodiments.

FIGS. 10A-10D illustrate plots of PIBF sensitivities for different bottomhole pressure (BHP) profiles, in accordance with certain embodiments.

FIG. 11 illustrates a schematic of an example framework of an artificial lift timing and selection (ALTS) workflow, in accordance with certain embodiments.

FIG. 12 illustrates an example process flow diagram illustrating a method of applying the ALTS workflow of FIG. 11 to forecast production using an artificial lift operation in a well penetrating a reservoir, in accordance with certain embodiments.

FIG. 13 illustrates a flow chart of an example method of applying the ALTS workflow of FIG. 11 to produce fluids using an artificial lift operation in a well penetrating a reservoir, in accordance with certain embodiments.

FIGS. 14A-14D illustrate plots of the ALTS workflow of FIG. 11 applied to a well using a sucker rod pump (SRP) for artificial lift, in accordance with certain embodiments.

FIGS. 15A and 15B illustrate plots of the ALTS workflow of FIG. 11 applied to a well using an electric submersible pump (ESP) for artificial lift hindcasted to show validity of the model, in accordance with certain embodiments.

FIGS. 16A-16C illustrate plots of the ALTS workflow of FIG. 11 applied to a well using an ESP for artificial lift for a three year forecast, in accordance with certain embodiments.

FIG. 17 illustrates a flow chart of an example method of applying the ALTS workflow of FIG. 11 to produce fluids using an artificial lift operation in a well penetrating a reservoir, in accordance with certain embodiments.

FIGS. 18A and 18B illustrate plots of the ALTS workflow of FIG. 11 applied to a well in hindcasting mode after natural flow to compare performance with different timings of artificial lift operations, in accordance with certain embodiments.

FIG. 19 illustrates a flow chart of an example method of applying the ALTS workflow of FIG. 11 to multiple wells for optimizing the production of fluids using gas lift injection equipment in multiple wells of a well pad, in accordance with certain embodiments.

FIGS. 20A and 20B illustrate plots of well level gas lift optimization.

FIG. 21 illustrates a plot of pad optimized gas lift performance curves, in accordance with certain embodiments.

FIG. 22 illustrates a block diagram showing an example information handling system, in accordance with certain embodiments.

While embodiments of this disclosure have been depicted and described and are defined by reference to exemplary embodiments of the disclosure, such references do not imply a limitation on the disclosure, and no such limitation is to be inferred. The subject matter disclosed is capable of considerable modification, alteration, and equivalents in form and function, as will occur to those skilled in the pertinent art and having the benefit of this disclosure. The depicted and described embodiments of this disclosure are examples only, and not exhaustive of the scope of the disclosure.

DETAILED DESCRIPTION

Illustrative embodiments of the present disclosure are described in detail herein. In the interest of clarity, not all features of an actual implementation may be described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions may be made to achieve the specific implementation goals, which may vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of the present disclosure.

The present disclosure relates to methods and systems for selecting the type, timing, and design parameters of artificial lift operations for use in one or more wells. In some embodiments, the artificial lift operations may implement a pragmatic ALTS system that employs a hybrid data-driven and physics-based approach which incorporates routinely accessible Pressure-Volume-Temperature (PVT) properties, rates, and pressure information and makes no assumptions about the homogeneity of the reservoir, the flow regimes, or the production mechanisms. Average reservoir pressure in drained volume and PI may be used to describe well deliverability as a dynamic IPR curve at various field conditions using dynamic drainage volume. Compared to the incumbent methods for artificial lift timing and selection, which are often based on heuristic and rule-based techniques, the disclosed ALTS system may be differentiated by this special characteristic to reflect subsurface performance. PI-based forecasts enable nodal analysis at any moment in the future and provide accurate oil and gas forecast estimations for various artificial lift techniques. Therefore, the ALTS system may operate to enable judgment on the optimal timing, economic, and effective selection of artificial lift types with optimized design parameters.

More specifically, the present disclosure provides methods including providing an artificial lift timing and selection (ALTS) workflow based on one or more hybrid data-driven and physics-based models to maximize the value of unconventional oil and gas assets. In some embodiments, the ALTS workflow may be used to calculate a transient well productivity index (PI) for a well at the field scale to analyze the performance of the well. The ALTS workflow may be used to forecast production using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, including: (a) receiving sensor feedback from the well during well production; (b) receiving a flowrate of oil, water, and gas for the well; (c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate; (d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure; (e) estimating well deliverability with vertical lift of the well, based at least in part on one or more artificial lift parameters; (f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well; (g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well; and (h) producing fluids from the well based, at least in part, on the estimated liquid, gas, water, and oil production of the well.

In addition, the present disclosure provides a method of producing fluids using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, comprising: (a) receiving sensor feedback from the well during well production; (b) receiving a flowrate of oil, water, and gas for the well; (c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate; (d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure; (e) estimating well deliverability with vertical lift of the well, based at least in part on one or more first artificial lift parameters for first artificial lift equipment; (f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability; (g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well; (h) performing steps (a)-(g) a second time for the well using one or more artificial lift parameters for second artificial lift equipment; (i) comparing the multiphase forecasts resulting from the first process of steps (a)-(g) using the one or more first artificial lift parameters and second process of steps (a)-(g) using the one or more second artificial lift parameters; (j) selecting artificial lift equipment to be used in the artificial lift operation based on the comparison of the multiphase forecasts; and (k) producing the fluids from the well using the selected type of artificial lift equipment.

In addition, the present disclosure provides a method of producing fluids using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, including: (a) receiving sensor feedback from the well during well production; (b) receiving a flowrate of oil, water, and gas for the well; (c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate; (d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure; (e) estimating well deliverability with vertical lift of the well, based at least in part on one or more first artificial lift parameters for first artificial lift equipment; (f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well; (g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well; (h) iteratively repeating steps (c)-(g) using a cumulative flowrate of the estimated liquid production of step (g) for the cumulative liquid rate of step (c) to provide a multiphase forecast of a plurality of values of the estimated liquid, gas, water, and oil production of the well over a pre-selected time period; (i) performing steps (a)-(h) a second time for the well using one or more second artificial lift parameters for second artificial lift equipment, the different one or more artificial lift parameters being indicative of artificial lift equipment being used during a different portion of the pre-selected time period; (j) comparing the multiphase forecasts resulting from the first process of steps (a)-(g) using the first one or more artificial lift parameters and second process of steps (a)-(g) using the one or more second artificial lift parameters; (k) selecting time period for use of the artificial lift equipment based on the comparison of the multiphase forecasts; and (l) producing the fluids from the well using the artificial lift equipment used during the selected time period.

In addition, the present disclosure provides a method of producing fluids using gas lift injection equipment in multiple wells of a well pad, each well extending through a subterranean formation, including: for each well of the multiple wells: (a) receiving sensor feedback from the well during well production; (b) receiving a flowrate of oil, water, and gas for the well; (c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate; (d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure; (e) estimating well deliverability with vertical lift of the well, based at least in part on one or more gas lift injection parameters; (f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well; (g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well; (h) generating a gas lift production (GLP) curve for the well based, at least in part, on the estimated oil production of the well; (i) comparing the GLP curves of the multiple wells; and (j) adjusting amounts of pressure output from a compressor to the gas lift injection equipment at two or more of the multiple wells based on the comparison of the GLP curves.

In some embodiments, the present disclosure provides a hybrid data-driven and physics-based process to improve artificial-lift timing, selection, and operating point of the lift system, to maximize the positive economic impact of a well. Existing techniques may not account for the effect of subsurface performance (e.g. reservoir drawdown and changes to inflow performance as a result of choosing the artificial lift type). Because of the type of artificial lift chosen and the settings that govern how the artificial lift system operates, the disclosed method, in certain embodiments, inherently creates a closed loop response when feedback is injected into the reservoir model. In some embodiments, the disclosed approach for hybrid reservoir modeling is based on assessing transient well performance (TWP). In certain embodiments, the disclosed approach is based on a formulation that integrates principles from the transient productivity index, succession of pseudo-steady-state material balance, and diffusive time of flight to estimate the deliverability of dynamic reservoirs. Utilizing PI-based forecasting (PIBF) approaches, this is paired with the vertical lift performance for various artificial lifts and their operational parameters to perform continuous nodal analysis and anticipate phase rates from the well.

The disclosed formulation, in certain embodiments, employs a reduced physics model that is based on the identification of Dynamic Drainage Volume (DDV) using commonly measured data (e.g., daily production rates and wellhead pressure) to calculate reservoir pressure depletion, transient productivity index (PI) and dynamic inflow performance relationship (IPR). Transient PI as the forecasting variable allows normalizing both surface pressure effects and considers phase behavior, thus reducing noise. The PIBF method is used to predict future IPRs and, subsequently, oil, water, and gas rates for any bottom hole pressure condition. The workflow allows estimating well deliverability under different artificial lift types, timing, and design parameters.

The disclosed ALTS workflow was applied to real field cases for wells flowing under different operating conditions to optimize the best strategy to produce the well amongst several candidate scenarios. Transient PI and dynamic IPR results provide valuable insights on how and when to select different artificial lift systems in accordance with present techniques. In certain embodiments, the disclosed workflow may be run periodically with everchanging subsurface and wellbore conditions against each candidate scenario with various pump models and other operating parameters (e.g., pressure, speed, etc.). The disclosed method was applied to several wells in a hindcasting mode to evaluate lost production opportunities and validate the results. In certain cases, the optimal recommendation pointed to selecting a different artificial lift system than the chosen method in the field to significantly improve long-term well performance. In addition, the disclosed ALTS workflow allows optimal artificial lift operating point recommendations to be made for wells, including optimal gas lift rates for gas-lifted wells, optimal pump unit selection, and speed for wells on ESP and SRP.

Among the many potential advantages to the methods and systems of the present disclosure, only some of which are alluded to herein, the methods of the present disclosure may allow predicting future unconventional reservoir IPR consistently and provide continuous evaluation of artificial lift timing and selection scenarios for multiple lift types and designs in unconventional reservoirs. This can transform incumbent practices based on broad field heuristics, which are often ad hoc, inefficient, and manually intensive, towards well-specific ALTS analysis to improve field economics. Continuous application of these methods is shown to improve production, reduce deferred production, and increase the life of lift equipment.

Artificial Lift Systems

In some embodiments, the artificial lift system may be configured to implement an efficient artificial lift mechanism for an unconventional well in terms of artificial lift system life-cycle management based on reservoir and fluid properties, wellbore configuration, and surface facility restraints. For example, the unconventional well may have a highly dynamic reservoir deliverability. Thus, the artificial lift system may be configured to implement one or more artificial lift methods which are adjusted to accommodate significant and quick changes in flowrate during the life of the unconventional well to apply artificial lift in a cost-effective manner and optimize expected ultimate recoveries from horizontal wells. As a result, several artificial lift methods may be applied at various production phases in the same well. Within a few months of production, horizontal wells frequently use two or more types of artificial lift. For example, after initial natural flow using the native reservoir energy, the wells may employ gas lift or electric submersible pumps (ESP) when they still flow at high rates. As well rates decline, other forms of artificial lift, such as a beam pump, a jet pump, a progressive cavity pump (PCP), a hydraulic pump, a sucker rod pump (SRP), a plunger lift, and a gas-assisted plunger lift (GAPL), etc., may be used. While other considerations are also necessary, such as gas-liquid ratio (GLR), wellbore configuration, sand production, power availability, operating costs, etc., the range of possible well production rates is a primary criterion.

In some embodiments, several factors may affect the decision to choose or reject an artificial lift system. To control enormous flow capacities, ESPs are often placed early in the well production process. However, managing fracturing sand and gas is one of the main reasons for most ESP failures. The typical run life of ESPs in certain basins is 3 to 6 months, which is seen to be an acceptable failure frequency. One of the most often used artificial lift systems for oil wells in various basins is the rod pump (also referred to as “beam pump”). The low normal lease operating expense (NLOE) and low failure frequency of beam pumps have led to numerous installations in unconventional wells. The key drawbacks of beam-pump deployment continue to be the ability to generate at a high rate and depth limitations depending on the structural load of the system. In unconventional reservoirs, gas lift has become a common lift system. This is primarily due to the desire to lower operational expenditures (OPEX) and lease operating costs and improved formation/fluid behavior understanding. In gas wells or wells with high GLR, plunger lifts are very economical and provide an economical alternative. An artificial lift selection strategy designed to maximize the value of unconventional oil and gas assets heavily incorporates economics. When commodity prices are low, decreasing the NLOE is critical to the economic viability of a well. Therefore, a proper selection of an artificial lift method for the unconventional well requires a thorough understanding of the system to offer different energy efficiencies.

In some embodiments, traditional methods for artificial lift selection may use a rule-based selection approach to develop a selection model which includes seven different kinds of artificial lifts. To assess the choice of artificial lift, the selection model of the artificial lift system may be determined using a plurality of factors, such as target liquid rate, highest predicted GLR, influence of dog leg severity (DLS) during operation, anticipated line pressure, and downhole temperature. Thus, the artificial lift system may be graded based on one or more outcomes of a set of rules that used the plurality of factors.

In some embodiments, one or more machine learning algorithms may be used in the artificial lift system to determine a best type of lift based on a plurality of field production parameters. For example, a machine learning model may be trained to examine artificial lift selection using about 50 production parameters, which may be categorized into well considerations, production circumstances, fluid characteristics, reservoir parameters, profitability aspects, surface facilities, HSE considerations, and supplier factors.

In some embodiments, the artificial lift system of the present disclosure may include one or more data-driven and physics-based models using a plurality of statistical measurements to categorize existing wells into peer groups based on key characteristics. Thus, the one or more data-driven and physics-based models may be used to generate production forecasts and BHP trends using a nodal analysis and the key characteristics. The nodal analysis is a system approach to the optimization of oil and gas wells by analyzing the performance of a producing system which includes a plurality of interacting components. Every component in the producing system may be optimized to achieve an objective flow rate most economically. For example, a new well may have a well profile created early on for use in choosing a suitable artificial lift system based on life cycle cost and benefits when combined with data on historical artificial lift performance. However, the nodal analysis may not account for PVT and multiphase flow behavior and is still dependent on type curves that are an average depiction of a collection of wells. Furthermore, by not accounting for reservoir deliverability or its forecast, the nodal analysis may not predict the performance of the selected well in the future.

In some embodiments, an artificial lift life cycle of a well may be associated with the performance, operations, and optimization of the well in the present and in the future as an ongoing process. Due to the dynamic nature of the well, the artificial lift system may be configured to implement proactive planning of all stages of the artificial lift life cycle for the best recovery by selecting a proper artificial lift method, changing a plurality of operational parameters, and planning the related workovers. The artificial lift system may also provide potential benefits, such as failure prevention, decreased failure rates, decreased costs and downtime, and improved operational efficiency by avoiding inappropriate artificial lift selection in the first place.

Transient Well Performance (TWP) Analysis

FIG. 1 illustrates a schematic representation of applications of a transient well performance (TWP) system 100, in accordance with certain embodiments. In some embodiments, TWP system 100 may include a real-time evaluation of how well performance is changing over time. TWP system 100 is pivoted on the development of an efficient technique for calculating the transient well PI for each well at the field scale to analyze the performance of unconventional wells. For example, TWP system 100 may make use of this transient PI as shown in FIG. 1, such as well performance analysis, well forecasting, artificial lift life cycle management, production optimization, and field development planning. In some embodiments, the TWP workflow, which is particularly practical in its use at a field scale, may be used in the disclosed ALTS method to optimize artificial lift timing and selection in unconventional wells.

FIG. 2 illustrates a schematic representation of a well performance analysis workflow 200, in accordance with certain embodiments. Well performance analysis workflow 200 may combine reduced physics and data-driven methodologies in a TWP workflow to describe well performance over a series of steps. Material balance may be applied through a succession of pseudo-steady states on the development of a drainage volume in a closed system to approximate transient well performance. For example, well performance analysis workflow 200 may include a plurality of applications to evaluate Pressure-Volume-Temperature (PVT) properties 202, flowing bottomhole pressure 204, dynamic drainage volume 206, transient well performance 208, and production forecasting 210. For example, PVT properties 202 are calculated in order to characterize a fluid behavior. Well performance analysis workflow 200 may estimate saturation pressure and the initial solution gas-oil ratio, corrected for separator conditions, using flowback data and a non-parametric regression method, such as Alternating Conditional Expectation (ACE) or a semi-analytical method. As another example, flowing bottomhole pressure 204 is computed using surface data for the whole production history to represent the downhole flowing conditions accurately. As another example, transient well performance 208 may be then estimated using an optimization approach, which calculates dynamic drainage volume 206, average reservoir depletion, and PI. PI is utilized as the base variable for production forecasting 210 under expected future operating conditions. Furthermore, PI also enables the estimation of inflow performance relationship (IPR) curves to capture well deliverability, a critical output required for production optimization.

In some embodiments, the key tenet of well performance analysis workflow 200 is that the drainage volume constantly increases over time, but the exact shape is not known. The reservoir withdrawal is known through cumulative volume produced, and the diffusive time of flight method for drainage volume calculation assures a no-flow outer boundary. Material balance may be carried out at each time step with great precision using the succession of pseudo-steady-state (SPSS) assumption (e.g. daily). In the contacted drainage volume at every timestep this enables the calculation of pressure depletion (e.g., average reservoir pressure) in the contacted drainage volume at any timestep.

In some embodiments, well performance analysis workflow 200 may make use of readily available production rates, PVT, and flowing bottomhole pressure. Additionally, well performance analysis workflow 200 may simulate both liquid and gas systems that are produced using any lift method (e.g., natural flow, gas lift, ESP, SRP, etc.). Likewise, well performance analysis workflow 200 may handle variable rate and bottomhole pressure, pressure depletion, variable compressibility, and non-linear pressure-dependent PVT characteristics.

The next few sections are focused on a discussion of the steps for estimation of dynamic IPR, which is the differentiating step to calculate actual reservoir deliverability or IPR continuously with time.

Diffusive Time of Flight (DTOF)

Diffusive time of flight (DTOF), representing the travel time of pressure front propagation, may be applied in multiple applications in unconventional reservoir performance analysis. The computation of DTOF typically involves an upwind finite difference of the Eikonal equation and solution using the fast-marching method (FMM)

The diffusive time of flight (DTOF) τ is physically associated with the peak propagation of a pressure pulse for an impulse source based on Equations 1 and 2. The 3D diffusivity equation can be reduced to a 1D diffusivity equation if the pressure gradients are assumed to be aligned with the time of flight τ gradients (i.e., p(, t)≈p(τ, t)). The diffusivity equation in heterogeneous porous media may be written as Equation 3:

$\begin{array}{cc}\sqrt{\alpha \left(\stackrel{\rightharpoonup }{x}\right)}❘\tau \left(\stackrel{\rightharpoonup }{x}\right)=1& \left(1\right)\\ \alpha \left(\stackrel{\rightharpoonup }{x}\right)=\frac{\stackrel{\rightharpoonup }{\stackrel{\rightharpoonup }{k}}\left(\stackrel{\rightharpoonup }{x}\right)}{\phi \left(\stackrel{\rightharpoonup }{x}\right)\mu c_t}& \left(2\right)\\ \phi \left(\stackrel{\rightharpoonup }{x}\right)\mu c_t\frac{\partial p\left(\stackrel{\rightharpoonup }{x},t\right)}{\partial t}=\nabla ·\left[\stackrel{\rightharpoonup }{\stackrel{\rightharpoonup }{k}}\left(\stackrel{\rightharpoonup }{x}\right)·\nabla p\left(\stackrel{\rightharpoonup }{x},t\right)\right]& \left(3\right)\end{array}$

• • where:
  • φ—porosity
  • μ—fluid viscosity
  • c_t—total compressibility
  • —permeability
  • p(, t)—pressure
  • α—diffusivity
  • τ—time of flight

Average Reservoir Pressure Estimation

In some embodiments, the first step in the workflow is to estimate the average reservoir pressure in the drained volume with time. The asymptotic version of the 1D diffusivity equation's boundary condition is solved to determine the drainage volume. The drainage volume is determined using the pressure and rate data in the absence of a well and reservoir model. The calculated drainage volume in Equation 4 represents the contacted reservoir pore volume due to the propagation of the pressure front at any given time step in the reservoir. Analogous to the concept of investigation radius in homogeneous fields, the calculated drainage volume tracks the DTOF contour of an irregular geometry due to the draining of a lumped fracture system, stimulated and unstimulated matrix. The rate normalized pressure (RNP) formulation represents the production behavior that would be observed if the well is produced at a constant reference rate.

$\begin{array}{cc}{V}_d\approx \frac{1}{c_t\frac{d}{{\mathrm{dt}}_e}\left(\mathrm{RNP}\right)}& \left(4\right)\end{array}$

• • where:
  • V_d—drainage volume
  • c_t—total compressibility
  • RNP—rate normalized pressure
  • t_e—material balance time

FIG. 3 illustrates a schematic representation of material balance 300 applied to a succession of pseudo-steady-state conditions with expanding drainage volume, in accordance with certain embodiments. In some embodiments, only under steady-state or pseudo-steady-state conditions, when the well drains a fixed volume and all reservoir boundaries have been reached, is material balance strictly valid. However, unconventional wells are seldom governed by boundary-dominated circumstances. The low permeability matrix may still be gradually touched even after the well contacts all fractures (loosely referred to as the stimulated rock volume or pseudo-boundary dominated flow).

As shown in FIG. 3, an expanding control volume strategy may remedy the issue. The outside edge of the control volume grows at the same rate as the pressure contours. Transient flow is approximated using “snapshots” or succession of instantaneous pseudo-steady states 302, 304, and 306. The size of the “container” in each time step is determined by the drainage volume, which was already estimated in Equation 4. There is no flow into the container according to the definition of drainage volume based on DTOF at each timestep. However, the amount that is removed from the system due to production at the well is known. Applying material balance allows for calculating the amount of energy lost in the drainage volume that was contacted and showed up as reservoir depletion. Daily tracking of cumulative output is done in the field, and these tiny timesteps also aid in increasing the precision of the material balance.

For liquid systems, the general material balance expression represents the following fundamental effects:

(Liquid expansion)+(Liberated gas expansion)+(Change in pore volume due to connate water/residual oil expansion and pore volume reduction)=(Underground withdrawal)

Putting it all together, Equation 5 describes the pressure change representing reservoir depletion for liquid systems:

$\begin{array}{cc}\Delta p=\left(\frac{1}{S_{\mathrm{wc}}{c}_w+S_{\mathrm{or}}{c}_o+c_f}\right)\left\{\frac{N_p\left[B_o+\left(R_p-R_s\right)B_g\right]+W_p{B}_w}{V_d}-\left(\frac{1-S_{\mathrm{wc}}-S_{\mathrm{or}}}{1+\omega }\right)\left[\frac{\left(B_o-B_{\mathrm{oi}}\right)+\left(R_{\mathrm{si}}-R_s\right)B_g}{B_o}+\frac{\omega \left(B_w-B_{\mathrm{wi}}\right)}{B_w}\right]\right\}& \left(5\right)\end{array}$

• • where:
  • B_o—oil formation volume factor at pressure of interest, bbl/STB
  • B_oi—oil formation volume factor at initial reservoir pressure, bbl/STB
  • B_w—water formation volume factor at pressure of interest, bbl/STB
  • B_wi—water formation volume factor at initial reservoir pressure, bbl/STB
  • R_si—initial solution gas-oil ratio, SCF/STB
  • R_s—solution gas-oil ratio at pressure of interest, SCF/STB
  • B_g—gas formation volume factor at pressure of interest, bbl/SCF
  • N_p—cumulative oil production, STB
  • S_wc—connate water saturation
  • S_or—residual oil saturation
  • c_o—oil compressibility, psi⁻¹
  • c_w—water compressibility, psi⁻¹
  • c_f—formation compressibility, psi⁻¹
  • W_p—cumulative water production, STB,
  • ω—ratio of water in place to oil in place
  • R_p—cumulative gas-oil ratio (calculated as cumulative gas divided by cumulative oil), SCF/STB

Note here,

$V_d=\frac{{\mathrm{NB}}_o+{\mathrm{WB}}_w}{1-S_{\mathrm{wc}}-S_{\mathrm{or}}},$

which is the total pore volume contacted which continuously expands as a function of time.

The delta-pressure in Equation 6 represents the reservoir pressure drop from initial pressure:

$\begin{array}{cc}\Delta p=p_i-p_{\mathrm{avg}}& \left(6\right)\end{array}$

• • where:
  • p_i—initial Reservoir Pressure (psi)
  • p_avg—Average Reservoir Pressure (psi)

The volumetric-averaged pressure in the contacted drainage volume at any given instant is represented by the average reservoir pressure, which roughly depicts the reservoir's depletion because of production.

Transient Productivity Index and Dynamic IPRs

In some embodiments, the productivity index (PI) may be an intrinsic property of the well indicative of well performance and true reservoir inflow potential. PI accounts for the impacts of PVT and pressure depletion and normalizes production volumes by flowing pressures. PI is a useful diagnostic metric to evaluate wells consistently under a variety of operating situations, such as fluctuating choke openings or artificial lift, hence removing biases present in traditional decline curve analysis (DCA) and type curves that are rate-time based and are frequent. Estimation of transient PI leads to a plethora of applications in production optimization and forecasting phase rates due to the knowledge about actual reservoir deliverability. For example, PI may be utilized for production optimization, predicting current and future production given alternative operating methods, by representing the relationship between rates and pressure drawdown.

In some embodiments, transient PI is a transient quantity in unconventional wells that is updated at each timestep. Transient PI may depend, at least in part, on the producing rates, flowing BHP, and average reservoir pressure. PI has a consistent value for a specific reservoir state at any time instance when the flowing BHP is above a saturation pressure (e.g., undersaturated). Due to relative permeability variations, gas liberation occurs when the flowing BHP falls below the saturation pressure, which lowers total liquid productivity index (PI). Given the pressure circumstances, it is possible to solve Equations 7, 8, and 9 to calculate the liquid PI at any given time step.

When both the reservoir and flowing BHP are above saturation pressure (i.e., undersaturated), the PI is defined as a simple linear equation based on Equation 7. Note that all terms on the right-hand side vary with time.

$\begin{array}{cc}{q}_l=\mathrm{PI}*\left(p_{\mathrm{avg}}-p_{\mathrm{wf}}\right)& \left(7\right)\end{array}$

• • where:
  • q_i—liquid rate, STB/D
  • PI—Productivity Index, STB/D/psi
  • p_avg—Average Reservoir Pressure (psi)
  • p_wf—Bottomhole Pressure (psi)

If the reservoir is still undersaturated, but the flowing BHP drops below saturation pressure (creating saturated conditions in the near-wellbore region), the following expression is used:

$\begin{array}{cc}{q}_l=\mathrm{PI}*\left(p_{\mathrm{avg}}-p_{\mathrm{sat}}\right)+\left(\frac{\mathrm{PI}*p_{\mathrm{sat}}}{1.8}\right)*\left[1-0.2\frac{p_{\mathrm{wf}}}{p_{\mathrm{sat}}}-0.8{\left(\frac{p_{\mathrm{wf}}}{p_{\mathrm{sat}}}\right)}^2\right]& \left(8\right)\end{array}$

• • where:
  • q_l—liquid rate, STB/D
  • PI—Productivity Index, STB/D/psi
  • p_avg—Average Reservoir Pressure (psi)
  • p_sat—Saturation Pressure (psi)
  • p_wf—Bottomhole Pressure (psi)

Finally, when the reservoir is fully saturated (average reservoir pressure has depleted below saturation pressure), the PI is represented as follows:

$\begin{array}{cc}{q}_l=\left(\frac{\mathrm{PI}*p_{\mathrm{avg}}}{1.8}\right)*\left[1-0.2\frac{p_{\mathrm{wf}}}{p_{\mathrm{avg}}}-0.8{\left(\frac{p_{\mathrm{qf}}}{p_{\mathrm{avg}}}\right)}^2\right]& \left(9\right)\end{array}$

• • where:
  • q_l—liquid rate, STB/D
  • PI—Productivity Index, STB/D/psi
  • p_avg—Average Reservoir Pressure (psi)
  • p_wf—Bottomhole Pressure (psi)

This makes it possible to create nodal analyses for any well at any time step by using IPR (inflow performance relationship) curves (as shown in FIG. 5) to depict true historical, current, and future well deliverability at each time step. FIGS. 4A and 4B illustrate a set of plots of oil productivity index for several wells from a liquid-rich shale play, in accordance with certain embodiments. FIG. 4A illustrates an oil PI versus time plot 400 for several wells from a liquid-rich US shale play. FIG. 4B illustrates an oil PI versus cumulative oil plot 450 for those wells. FIG. 5 illustrates a set of plots showing the dynamic inflow performance relationship (IPR) 500 of a well, in accordance with certain embodiments. FIG. 5 illustrates dynamic IPR curves 502 of a well plotted every month with average rates shown as white points.

Multiphase PI-Based Forecasting (PIBF)

In some embodiments, traditional DCA only functions when operating conditions are constant and ignores pressure data (e.g., constant bottomhole pressure or choke setting, with limited production interruptions) and fluid phase behavior (e.g., PVT property changes). To normalize both surface impacts and take into account phase behavior and therefore reduce noise, the disclosed ALTS workflow may be programmed to utilize PI as a forecasting variable. This provides clearer patterns that are easy to fit, which leads to more precise models and better forecasts. Additionally, since a definite production decline trend may be established earlier, it is feasible to produce the forecast with lesser data during early production life of the well.

In some embodiments, liquid rate forecasts are made from a base PI forecast utilizing an ensemble of decline models, reservoir pressure, and flowing BHP extrapolation. Initially, the liquid PI trend is fitted with a modified hyperbolic equation. Other time series-based forecasting models, such as autoregressive integrated moving average (ARIMA), recurrent neural network (RNN), etc., may also be applied based on past values of PI and cumulative volumes based on Equation 10.

$\begin{array}{cc}{\mathrm{PI}}_t=\frac{{\mathrm{PI}}_0}{{\left(1+b\left(N_p+W_p\right)\right)}^{\frac{1}{a}}}=f\left(t,N_p+W_p,{\mathrm{PI}}_{t-k}\right)& \left(10\right)\end{array}$

• • where:
  • N_p—cumulative oil production, STB
  • W_p—cumulative water production, STB
  • PI—Productivity Index, STB/D/psi

In some embodiments, the PI forecast is converted into a liquid rate profile by combining it with reservoir pressure and BHP forecasts, using Equations 7, 8, and 9. A history matching process is used to define the profile, and the material balance formulation as stated in the previous phase is used to extrapolate the average reservoir pressure as a function of cumulative liquid, according to the profile. Since the BHP profile provides the anticipated operating conditions under the intended production strategy for each well, the operator has entire control over it (e.g., choke schedule, artificial lift installs, and operational set points). As a result, the BHP profile can either be a smooth profile or a segmented function indicating several drawdowns associated with the use of different production techniques. The BHP profile can even become a sensitivity tool to evaluate the production impact of different operational strategies or normalize production conditions to compare different wells using a common BHP profile.

In some embodiments, after a single-phase forecast of the liquid rate is established, a multiphase forecast may be derived to determine the corresponding oil, gas, and water rate profiles. Two steps are used to do this: first, model the water cut, and then model the gas-oil ratio (GOR). Both the water cut and GOR models are independent and modular, and different mathematical representations may be utilized as part of the process and are customized to a specific field's characteristics.

In some embodiments, modeling GOR in unconventional reservoirs has been a major challenge as compared to that in conventional reservoirs due to a more complex fluid flow behavior. In unconventional reservoirs, several studies have shown GOR behavior is driven by BHP schedule, the degree of undersaturation, fracture parameters, gas-oil PVT properties, relative permeabilities, bubble point suppression effects and duration of the transient-flow regime. This can be achieved with the material balance formulation based on the dynamic drainage volume derived above. Rearranging the material balance formulation in terms of R_p, which is the cumulative GOR of a well, gives Equation 11. As shown, the formulation depends on PVT properties, production data, reservoir properties, drainage volume, and drawdown at a given time. As a result, to forecast GOR, all the PVT properties computed at forecasted average reservoir pressure and the forecasted liquid phase rates computed during the previous step are used. This reduced physics formulation for GOR captures complex non-linearities associated with PVT changes as the fluids are produced.

$\begin{array}{cc}{R}_p=R_s+\frac{V_d}{N_p{B}_g{B}_{\mathrm{oi}}}\left[\begin{array}{c}\left(R_{\mathrm{si}}-R_s\right)B_g+\left(B_o-B_{\mathrm{oi}}\right)+\\ \omega \frac{\left(B_w-B_{\mathrm{wi}}\right)B_{\mathrm{oi}}}{B_{\mathrm{wi}}}+\frac{(S_{\mathrm{wc}}{c}_w+S_{\mathrm{or}}{c}_i+c_f}{\left(1-S_{\mathrm{wc}}-S_{\mathrm{or}}\right)}\left(1+\omega \right)B_{\mathrm{oi}}\Delta p\end{array}\right]-\frac{W_p{B}_w}{N_p{B}_g}-\frac{B_o}{B_g}& \left(11\right)\end{array}$

• • where:
  • B_o—oil formation volume factor at pressure of interest, bbl/STB
  • B_oi—oil formation volume factor at initial reservoir pressure, bbl/STB
  • B_w—water formation volume factor at pressure of interest, bbl/STB
  • B_wi—water formation volume factor at initial reservoir pressure, bbl/STB
  • R_si—initial solution gas-oil ratio, SCF/STB
  • R_s—solution gas-oil ratio at pressure of interest, SCF/STB
  • B_g—gas formation volume factor at pressure of interest, bbl/SCF
  • N_p—cumulative oil production, STB
  • S_wc—connate water saturation
  • S_or—residual oil saturation
  • c_o—oil compressibility, psi⁻¹
  • c_w—water compressibility, psi⁻¹
  • c_f—formation compressibility, psi⁻¹
  • W_p—cumulative water production, STB,
  • ω—ratio of water in place to oil in place
  • R_p—cumulative gas-oil ratio (calculated as cumulative gas divided by cumulative oil), SCF/STB

FIG. 6 illustrates a flow chart of an example method for performing productivity index (PI) based forecasting (PIBF) of liquid, oil, water, and gas rates, in accordance with certain embodiments. Method 600 of FIG. 6 may be used by well performance analysis workflow 200 of FIG. 2. Method 600 starts at step 605, well performance analysis workflow 200 (referring to FIG. 2) may calculate liquid PI and average reservoir pressure from dynamic drainage volume. For example, the liquid PI may be determined for given pressure properties at any given time step based on Equations 7, 8, and 9. As another example, the average reservoir pressure is a volumetric-averaged pressure in a contacted drainage volume at any timestep.

At step 610, well performance analysis workflow 200 (referring to FIG. 2) may fit the liquid PI based on a forecasting model. For example, the forecasting model may include a modified hyperbolic model, an ARIMA model, an RNN model, etc.

At step 615, well performance analysis workflow 200 (referring to FIG. 2) may forecast a liquid rate with a plurality of abandonment constraints. The liquid rate may be determined based on a base PI forecast utilizing an ensemble of decline models, reservoir pressure, and flowing BHP extrapolation. The plurality of abandonment constraints may include abandonment gas rate, abandonment liquid rate, abandonment water rate, and abandonment oil rate for the forecast. When any of the plurality of abandonment constraints is reached, the forecast may end.

At step 620, well performance analysis workflow 200 (referring to FIG. 2) may forecast one or more oil and water rates with plurality of abandonment constraints by fitting water cut.

At step 625, well performance analysis workflow 200 (referring to FIG. 2) may forecast a gas rate with the plurality of abandonment constraints based on a gas-oil ratio model.

Particular embodiments may repeat one or more steps of the method of FIG. 6, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG. 6 as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 6 occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to perform the well performance analysis flow for a reservoir system, including the particular steps of the method of FIG. 6, this disclosure contemplates any suitable method, including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 6, where appropriate. In some embodiments, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 6, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 6.

PI Based Forecasting (PIBF)

FIGS. 7A-7D illustrate plots of PIBF of liquid rates and oil rates for a liquid well, in accordance with certain embodiments. In some embodiments, the disclosed ALTS workflow is implemented to forecast against different lift strategies based on PIPF. FIGS. 7A-7D illustrate a PIBF example for a well with PI liquids forecasted and compared against actual liquid values. In particular, FIG. 7A shows forecast liquid PI values 706 compared against historical liquid PI values 702 and actual validation liquid PI values 704. The liquid PI trend is fitted using historical liquid PI values 702 as a function of cumulative liquids to allow its dependency on the rate of drawdown with time. FIG. 7B shows forecast BHP values 716 compared against historical BHP values 712 and actual validation BHP values 714. Historical BHP values 712 are used for forecasting the liquid and oil rates. The BHP profile during the forecast period is chosen to be the same as the actual BHP profile for true comparison. FIG. 7B shows liquid rate forecasts 726 compared against historical liquid rates 722 and actual validation liquid rates 724. FIG. 7D shows oil rate forecasts 736 compared against historical oil rates 732 and actual validation oil rates 734. As can be seen, the liquid rate forecasts 726 show a good match with the actual validation liquid rates 724 within a 5% mean absolute percentage error (MAPE). Likewise, the oil rate forecasts 736 show a good match with the actual validation oil rates 734 within a 5% MAPE.

FIGS. 8A-8D illustrate plots of PIBF of gas rates for two different wells, in accordance with certain embodiments. Once the liquid and oil forecasts are available, the reduced physics formulation based on Equation 11 is then used to determine a reduced physics model to forecast GOR and hence the gas rates. FIGS. 8A-8D show two well examples with gas rates and GOR forecasts. In particular, FIG. 8A shows forecast gas rates 806 compared against historical gas rates 802 and actual validation gas rates 804 for a first well. FIG. 8B shows forecast cumulative GOR 816 compared against historical cumulative GOR 812 and actual validation cumulative GOR 814 for the first well. FIG. 8C shows forecast gas rates 826 compared against historical gas rates 822 and actual validation gas rates 824 for a second well. FIG. 8D shows forecast cumulative GOR 836 compared against historical cumulative GOR 832 and actual validation cumulative GOR 834 for the second well. The reduced physics model may capture the complex nonlinearities associated with the GOR forecasts due to PVT and BHP variations and show reasonable trends compared to the actual values.

Drawdown Management

FIG. 9 illustrates a plot 900 of an IPR curve with a realistic PI alongside an IPR curve represented using a simplified straight line method, in accordance with certain embodiments. In some embodiments, a well performance analysis (WPA) workflow application that is based on the actual reservoir deliverability determined by transient PI will now be described. By adjusting for surface impacts and shifting operational conditions, PIBF offers a more reliable technique to anticipate future production. The pressure drawdown is a key factor in determining the predicted production profile in unconventional wells, and it is managed by the operator through operations like choke adjustments and artificial lift. The link between increasing output and larger pressure drawdown is not linear. FIG. 9 compares a Vogel-type PI 902 with a linear pressure normalized rate (PNR) 904, which is frequently employed in place of PI.

FIGS. 10A-10D illustrate plots of PIBF sensitivities for different bottomhole pressure (BHP) profiles, in accordance with certain embodiments.

In some embodiments, FIG. 10A shows two BHP profiles for future rate forecasts, such as forecast BHP values 1006 and 1008, compared against historical BHP values 1002. FIG. 10B shows cumulative liquid forecasts for the two BHP profiles, such as forecast cumulative liquid values 1016 and 1018, compared against historical cumulative liquid values 1012. FIG. 10C shows cumulative oil forecasts for the two BHP profiles, such as forecast cumulative oil values 1026 and 1028, compared against historical cumulative oil values 1022. FIG. 10D shows cumulative GOR forecasts for the two BHP profiles, such as forecast cumulative GOR values 1036 and 1038, compared against historical cumulative GOR values 1032. Since PI normalizes for surface and bottom hole pressure variations, PI forecasts can help identify phase rates under any BHP constraint, as shown.

Artificial Lift Timing and Selection Workflow

In some embodiments, the transient WPA procedure may be implemented to enable assessment of reservoir deliverability using dynamic IPRs at any point in the future. Since cumulative liquids produced as in Equation 10 constitute a factor in PI estimates, different drawdown procedures result in varied transient IPRs based on projected rates. Nodal analysis can be carried out utilizing vertical lift performance (VLP) estimations of various lift types on a continuous basis, providing oil, water, and gas rate projections based on PI-based forecasting approach with the availability of real reservoir deliverability.

In some embodiments, IPR may be used at the beginning of the prediction and dynamically updated to compute the operating point at each time step to assess the well's performance for every lift type. Because of the choice of artificial lift type and operating settings for that AL, an implicit closed loop response may be obtained by constantly injecting feedback to the reservoir at each timestep.

FIG. 11 illustrates a schematic of an example framework 1100 of an artificial lift timing and selection (ALTS) workflow, in accordance with certain embodiments. In some embodiments, in block 1102 at the beginning of the prediction, the ALTS workflow may be implemented to determine the liquid PI (PI) and average reservoir pressure (P_avg) based on the cumulative liquid rate from prior time or the most recent point in history. This formulation is thus explicit in time. In block 1104, the ALTS workflow may be implemented to estimate the reservoir deliverability at the first time-step using IPR based on the determined liquid PI (PI), average reservoir pressure (P_avg), and other relevant information. Additionally, the watercut (WC), gas-liquid ratio (GLR), well head pressure (P_wh) and the lift parameters for the chosen lift type are all used to determine VLP 1106. In block 1108, the ALTS workflow may be implemented to apply nodal analysis to determine the operating point or bottomhole pressure (P_wf) using the reservoir deliverability (IPR) and well deliverability (VLP) estimates at the present time step. To estimate oil and water predictions, the same process is then used as with the PIBF described above. Finally, as demonstrated in the preceding section, GOR and consequently gas rates may be evaluated using the simplified physics-based model of Equation 11. As a result, in the next time step, these inputs may be used to determine the PI, average reservoir pressure, and ultimately IPR and VLP curves. Thus, until the prediction period is given, this iterative process may be repeated.

In some embodiments, the lift parameters may include parameters that are indicative of a particular type of artificial lift equipment for use in the well. In addition, any control design parameters for the chosen artificial lift type that one may be interested in running sensitivity for can be used as the lift parameters. For instance, one can select several ESP pump types, based on their unique pump curves, frequency, number of stages, well head pressures, etc. For a rod pump, one may select various rod lengths, speeds (strokes per minute), plunger specifications, well head pressures, etc. For gas lift, the lift parameters may include gas lift rates and wellhead pressures. By predicting against the various parameters, or designs that are accessible, these control parameters may be improved for a certain lift type.

FIG. 12 illustrates a flow chart of an example method of applying the ALTS workflow of FIG. 11 to forecast production using an artificial lift operation in a well penetrating a reservoir, in accordance with certain embodiments. The method 1200 may include, at block 1202, receiving sensor feedback from the well during well production. The sensor feedback may be determined by using one or more sensors which are configured to measure physical properties of the subsurface, such as pressure, temperature, fluid flow, and saturation. The method 1200 may also include, at block 1204, receiving a cumulative flowrate of liquid for the well.

The method 1200 may include, at block 1206, calculating an average reservoir pressure and a productivity index (PI) of the well based on the cumulative liquid rate. The method 1200 may include, at block 1208, estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure. The method 1200 may include, at block 1210, estimating well deliverability with vertical lift of the well, based at least in part on one or more artificial lift parameters in block 1212. The method 1200 may include, at block 1214, using a nodal analysis of the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well.

The method 1200 may include, at block 1216, conducting a multiphase forecast (e.g., PIBF) of an estimated liquid, gas, water, and oil production of the well, based on the estimated BHP and the sensor feedback from the well. The multiphase forecast (block 1216) may be conducted based on a transient PI and a plurality of PVT properties for the well at the average reservoir pressure. The transient PI may be calculated based on the estimated BHP, and the plurality of PVT properties may be computed based on the sensor feedback from the well. The PVT properties may include at least, for example, solution gas-oil ratio, gas formation volume factor, oil formation volume factor, and water formation volume factor. The PVT properties may be used to estimate a cumulative GOR, as discussed above. The multiphase forecast (block 1216) may be conducted based on the transient PI and the cumulative GOR.

The method 1200 may include, at block 1218, producing fluids from the well based, at least in part, on the estimated liquid, gas, water, and oil production of the well.

In embodiments, the method 1200 may include iteratively repeating the steps in blocks 1206-1216 using a cumulative flowrate of the estimated liquid production of block 1216 for the cumulative flowrate of block 1206. This may ultimately provide a multiphase forecast of a plurality of values of the estimated liquid, gas, water, and oil production of the well over a pre-selected time period. In such instances, block 1218 may include producing the fluids from the well based, at least in part, on the multiphase forecast having the plurality of values.

In certain embodiments, the iterative process of blocks 1206-1216 may be repeated a second time for the well using a different one or more artificial lift parameters (block 1212). The multiphase forecasts resulting from the first iterative process using the one or more artificial lift parameters and the second iterative process of steps using the different one or more artificial lift parameters may be compared, and a particular artificial lift operation selected based on the comparison. In such instances, block 1218 may include producing the fluids from the well using the selected artificial lift operation.

To demonstrate the benefits of the ALTS workflows discussed above, the workflows were applied to several field cases in the following Examples 1-5.

Particular embodiments may repeat one or more steps of the method of FIG. 12, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG. 12 as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 12 occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to perform the ALTS workflow to provide a multiphase forecast of a plurality of values of the estimated liquid, gas, water, and oil production of a well over a pre-selected time period, including the particular steps of the method of FIG. 12, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 12, where appropriate. In some embodiments, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 12, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 12.

FIG. 13 illustrates a flow chart of an example method of applying the ALTS workflow of FIG. 11 to produce fluids using an artificial lift operation in a well penetrating a reservoir, in accordance with certain embodiments.

In FIG. 13, it should be noted that blocks 1302, 1304, 1306, and 1308 are intended to represent the steps shown in blocks 1202-1216 of FIG. 12 as shown and described above. Similarly, in FIG. 13 blocks 1302, 1304, 1310, and 1312 are intended to represent the steps shown in blocks 1202-1216 of FIG. 12 as shown and described above. FIG. 13 shows the same process being performed twice, once with a first set of artificial lift parameters (block 1306) and another time with a second set of artificial lift parameters (block 1310). The second set of artificial lift parameters may be indicative of different artificial lift equipment than the first set of artificial lift parameters. Different artificial lift equipment may include, for example, a different type of artificial lift equipment (e.g., ESP, SRP, gas lift, etc.), or a same type of artificial lift equipment having different operational properties.

The method 1300 may include, at block 1314, comparing the multiphase forecast (block 1308) resulting from the first process using the first one or more artificial lift parameters (block 1306) with the multiphase forecast (1312) resulting from the second process using the second one or more artificial lift parameters (1310). The method 1300 may include, at block 1316, selecting artificial lift equipment to be used in the artificial lift operation based on the comparison of the multiphase forecasts. The method 1300 may then include, at block 1318, producing the fluids from the well using the selected artificial lift equipment.

To demonstrate the benefits of the ALTS workflows discussed above, the workflows were applied to several field cases in the following Examples 1-3.

Particular embodiments may repeat one or more steps of the method of FIG. 13, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG. 13 as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 13 occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to perform the ALTS workflow to produce fluids using an artificial lift operation in a well penetrating a reservoir, including the particular steps of the method of FIG. 13, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 13, where appropriate. In some embodiments, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 13, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 13.

To facilitate a better understanding of the present disclosure, the following examples of certain aspects of preferred embodiments are given. The following examples are not the only examples that could be given according to the present disclosure and are not intended to limit the scope of the disclosure or claims.

Example 1—Oil Well Installed with Sucker Rod Pump

In this example, an oil well from a major shale basin in North America is considered, which was installed with an SRP early in its life. The operator was interested in evaluating a what-if scenario or lookback analysis on how the well could have performed with alternate lift strategies.

FIGS. 14A-14D illustrate plots of the ALTS workflow of FIG. 11 applied to a well currently using a sucker rod pump (SRP) for artificial lift, in accordance with certain embodiments. The future forecast case for the well on rod pump is shown in FIGS. 14A-14D. Two separate 1-year forecast profiles are displayed, one for the current setup of the rod pump running at 3.5 strokes per minute. The other profile consists of a proposed ESP case with a small ESP pump that runs at a 50 Hz frequency.

FIG. 14A shows a nodal analysis of IPR at the start of the forecast 1402, IPR at the end of the forecast for SRP 1404, IPR at the end of the forecast for ESP 1406, VLP at the end of the forecast for SRP 1408, and VLP at the end of the forecast for ESP 1410. FIG. 14B shows the resulting oil rate forecast for SRP 1412 and the resulting oil rate forecast for ESP 1414. FIG. 14C shows the resulting gas rate forecast for SRP 1422 and the resulting gas rate forecast for ESP 1424. FIG. 14D shows the resulting GOR forecast for SRP 1432 and the resulting GOR forecast for ESP 1434. The oil and gas production profile for ESP is a little bit better, but not significantly better so as to make up for the expense of switching from the SRP to ESP. Additionally, the ESP's larger drawdown causes a quicker increase in GOR than the SRP does.

Example 2—Oil Well Installed with ESP

In this example, another oil well from a North American field was chosen, which was installed with an ESP. The goal in this example was to determine the right pump operating parameters and whether ESP or SRP would benefit the long-term performance due to drawdown management.

FIGS. 15A and 15B illustrate plots of the ALTS workflow of FIG. 11 applied to a well currently using an electric submersible pump (ESP) for artificial lift hindcasted to show validity of the model, in accordance with certain embodiments. In particular, FIGS. 15A and 15B show a validation case study for a well on ESP. The procedure was applied in this instance to validate the model outputs by performing a hindcasting scenario. Three more scenarios were also run to examine the effectiveness of various lift configurations. These configurations comprise a rod pump scenario at a fixed speed and two additional ESPs with various well head pressures and frequencies.

FIG. 15A shows a plurality of oil rate forecasts for the well: oil rate forecast under the original ESP 1502, oil rate forecast under a first additional ESP configuration 1504, oil rate forecast under a second additional ESP configuration 1506, and oil rate forecast under the rod pump configuration 1508. FIG. 15B shows the a plurality of gas rate forecasts for the well: gas rate forecast under the original ESP 1512, gas rate forecast under a first additional ESP configuration 1514, gas rate forecast under a first under a second additional ESP configuration 1516, and the gas rate forecast under the rod pump configuration 1518. The plurality of model forecasts of oil and gas rates are validated by the base case scenario (original ESP), which corresponds to the configuration present at around 800 days into the well's life. The original ESP configuration turns out to be the best case among the other suggested situations as well.

Example 3—Well Installed with ESP and Declining in Production

In the next example, a well is equipped with an ESP and is declining in production. It is desirable to evaluate if a smaller size pump or an SRP would perform better than the current pump. Also, the expected life expectancy of each pump is considered in economic calculations that include installation and replacement costs, maintenance costs, and potential forecasted revenues. FIGS. 16A-16C illustrate plots of the ALTS workflow of FIG. 11 applied to a well currently using an ESP for artificial lift for a three year forecast, in accordance with certain embodiments. In particular, FIGS. 16A-16C show a long term forecast scenario for the well on ESP. Additional artificial lift sensitivities were run with a smaller ESP and an SRP. The forecasts are run for a period of three years to evaluate the long-term performance under different artificial lift types along with economic analysis. For economic analysis, there is an install cost associated for switching to new lift types and there is a reinstall cost associated with each case, which leads to a drop in cumulative discounted cash flow curve at a fixed frequency of life expectancy for a given lift type.

FIG. 16A shows a plurality of oil rate forecasts for the well: oil rate forecast under the original ESP 1602, oil rate forecast under the smaller ESP configuration 1604, and oil rate forecast under the SRP configuration 1606. FIG. 16B shows a plurality of gas rate forecasts for the well: gas rate forecast under the original ESP 1612, gas rate forecast under the smaller ESP configuration 1614, and gas rate forecast under the SRP configuration 1616. FIG. 16C shows a plurality of cumulative discounted cashflow forecasts for the well: cumulative discounted cashflow forecast under the original ESP 1632, cumulative discounted cashflow forecast under the smaller ESP configuration 1634, and cumulative discounted cashflow forecast under the SRP configuration 1636. For each of the plurality of cumulative discounted cashflow forecasts include a drop in cashflow occurring due to reinstall cost. Based on the results, the existing ESP configuration outperforms the other scenarios both in terms of production and the economics that does not justify having a smaller ESP or SRP installed.

Example 4—Timing Selection for Artificial Lift Equipment

FIG. 17 illustrates a flow chart of an example method of applying the ALTS workflow of FIG. 11 to produce fluids using an artificial lift operation in a well penetrating a reservoir, in accordance with certain embodiments. In FIG. 17, blocks 1702, 1704, 1706, and 1708 are intended to represent the steps shown in blocks 1202-1216 of FIG. 12 as shown and described above. Similarly, in FIG. 17 blocks 1702, 1704, 1710, and 1712 are intended to represent the steps shown in blocks 1202-1216 of FIG. 12 as shown and described above. FIG. 17 shows the same process being performed twice, once with a first set of artificial lift parameters (block 1706) and another time with a second set of artificial lift parameters (block 1710). The second set of artificial lift parameters may be indicative of the same artificial lift equipment as in the first set of artificial lift parameters, but being used during a different portion of a pre-selected time period for which the multiphase forecasts are prepared.

The method 1700 may include, at block 1714, comparing the multiphase forecast (block 1708) resulting from the first process using the first one or more artificial lift parameters (block 1706) with the multiphase forecast (1712) resulting from the second process using the second one or more artificial lift parameters (1710). The method 1700 may include, at block 1716, selecting a time period for use of the artificial lift equipment based on the comparison of the multiphase forecasts. The method 1700 may then include, at block 1718, producing the fluids from the well using the artificial lift equipment during the selected time period.

FIGS. 18A and 18B illustrate plots of the ALTS workflow of FIG. 11 applied to a well in hindcasting mode after natural flow to compare performance with different timings of artificial lift operations, in accordance with certain embodiments. The example shown in FIGS. 18A and 18B demonstrates how the workflow may be utilized to determine when to switch to the appropriate lift type. In this case, SRP was installed in a well relatively early due to unstable natural flow. The lift system was switched to an ESP after running the well with the SRP for nearly two years. Alternate production scenarios that may have been developed using various lift methods can be evaluated by using the disclosed ALTS methodology.

FIG. 18A shows a nodal analysis of: IPR at the start of the forecast 1802; IPR at the end of the forecast for SRP 1804, IPR at the end of the forecast for ESP 1808; outflow at the end of the forecast for SRP 1806, and outflow at the end of the forecast for ESP 1810. FIG. 18B shows an oil rate forecast for SRP 1812 and an oil rate forecast for ESP 1814. According to the oil rate profile for the ESP case, installing an ESP in place of the SRP early in this example might have resulted in an additional volume of oil of about 60k STB.

Example 5—Operational Design for Multiple Gas Lifted Wells

FIG. 19 illustrate a flow chart of an example method of applying the ALTS workflow of FIG. 11 to multiple wells for optimizing the production of fluids using gas lift injection equipment in multiple wells of a well pad, in accordance with certain embodiments. In particular, FIG. 19 shows a process flow diagram of a method 1900 applying the ALTS workflow of FIG. 11 to produce fluids using gas lift injection equipment in multiple wells of a well pad, each well extending through a subterranean formation.

In FIG. 19, a portion of the process in FIG. 12 is performed multiple times, e.g., once for a first well (block 1902A) and again for a second well (block 1902B). It should be noted that blocks 1904A/B, 1906A/B, 1908A/B, and 1910A/B are intended to represent the steps shown in blocks 1202-1216 of FIG. 12 as shown and described above. Gas lift injection parameters 1908A/B for each well 1902A/B are used in estimating the well deliverability with vertical lift of the well. For each well 1902A/B, the method 1900 further includes, at blocks 1912A/B, generating a gas lift production (GLP) curve for the well.

The method 1900 may include, at block 1914, comparing the GLP curves (1912A/B) of the multiple wells. The method 1900 may include, at block 1916, adjusting amounts of pressure output from a compressor to the gas lift injection equipment at two or more of the wells based on the comparison of the GLP curves.

Example 5 examines how operational design recommendations for gas lifted wells may be made using the disclosed ALTS methodology. In general, gas lift designs with poor inflow performance result in ineffective operations, which means they lift less quickly than intended. By continually generating gas lift performance curves at various gas lift rates under the current flow circumstances, the TWP approach may optimize an objective function, such as maximum production or an economic measure at the well or pad level.

FIGS. 20A and 20B illustrate plots of well level gas lift optimization. Gas lift nodal analysis is used on one of the wells in a significant unconventional play in the case given below. FIGS. 20A and 20B display gas lift performance curve for this well using disclosed TWP procedure one of its historical days, as well as the IPR and VLP curves from the system analysis for that day. In particular, FIG. 20A shows the current operating point 2002, the GLP curve 2004, minimum gas lift rate to prevent liquid loading as indicated by a vertical line 2008, and the optimal operating point 2006, and FIG. 20B shows an IPR curve 2012 and a VLP curve 2014 used in the nodal analysis for the same well. Likewise, FIG. 20B shows actual data 2016 and a VLP curve 2018 for one of the sensitivities at a different gas lift rate. Sensitivities are performed continuously at various gas lift rates, as can be observed on the system analysis plot, allowing for the determination of the gas lift performance curves. The present operating point 2020 is indicated on the GLP curve, and an appropriate gas lift rate to inject is advised leading to 33 STB/D of oil uplift based on the objective function to maximize profit and prevent liquid loading.

Optimizing gas lift injection based on water handling and compression restrictions is crucial in unconventional production. The overall compression accessible to the pad (FIG. 21) is considered in the aforementioned example and distributed across all the wells on the pad that are linked to the same compressor. As was previously mentioned, optimization often happens where the gas lift performance curve flattens out, and compression increases have little potential for improvement.

FIG. 21 illustrates a plot of pad optimized gas lift performance curves, in accordance with certain embodiments. In particular, FIG. 21 shows a plurality of liquid production rate for four different wells: a liquid production rate for a first well 2102, a liquid production rate for a second well 2104, a liquid production rate for a third well 2106, a liquid production rate for a fourth well 2108. Likewise, FIG. 21 shows actual gas injection rates 2112 and optimal gas injection rates 2114 for the four wells. The process automatically determines the ideal injection rate for the whole pad of wells by using the compressor and deliverable gas restrictions. Due to excessive injection, two of the wells on the pad have too much backpressure, while the other two are injecting insufficiently compared to their ideal levels. To maximize output both on the well and across the pad, gas can be redistributed based on the gas lift performance curve. To maximize output and to reduce excess gas lift (lifting expenses) or backpressure in the wells, the quantity of gas is distributed across the pad based on the total amount of deliverable gas injection.

Optimizing gas lift injection based on water management and compression restrictions is desirable in unconventional reservoir production. In this example, the total compression accessible to the pad is considered and distributed across all the wells linked to the same compressor on the pad. As was previously mentioned, optimization often happens where the gas lift performance curve flattens out, and compression increases have little potential for improvement.

Hardware

FIG. 22 illustrates a block diagram of an exemplary control unit 2200 in accordance with some embodiments of the present disclosure. In certain example embodiments, control unit 2200 may be configured to create and maintain one or more databases 2208 that include information concerning one or more reservoirs or reservoir models, one or more wells or well models, or a combination thereof. In some embodiments, control system 2202 may include one more processors, such as processor 2204. Processor 2204 may include, for example, a microprocessor, microcontroller, digital signal processor (DSP), application specific integrated circuit (ASIC), or any other digital or analog circuitry configured to interpret and/or execute program instructions and/or process data. In some embodiments, processor 2204 may be communicatively coupled to memory 2206. Processor 2204 may be configured to interpret and/or execute non-transitory program instructions and/or data stored in memory 2206. Program instructions or data may constitute portions of software for carrying out estimations well deliverability, reservoir deliverability, and BHP using nodal analysis, as described herein. Memory 2206 may include any system, device, or apparatus configured to hold and/or house one or more memory modules; for example, memory 2206 may include read-only memory, random access memory, solid state memory, or disk-based memory. Each memory module may include any system, device or apparatus configured to retain program instructions and/or data for a period of time (e.g., computer-readable non-transitory media).

Although control unit 2200 is illustrated as including two databases 2208, control unit 2200 may contain any suitable number of databases. Control unit 2200 may be communicatively coupled to one or more displays 2216 such that information processed by control system 2202 may be conveyed to operators at or near the well or may be displayed at a location offsite.

Modifications, additions, or omissions may be made to FIG. 22 without departing from the scope of the present disclosure. For example, FIG. 22 shows a particular configuration of components for control unit 2200. However, any suitable configurations of components may be used. For example, components of control unit 2200 may be implemented either as physical or logical components. Furthermore, in some embodiments, functionality associated with components of control unit 2200 may be implemented in special purpose circuits or components. In other embodiments, functionality associated with components of control unit 2200 may be implemented in a general purpose circuit or components of a general purpose circuit. For example, components of control unit 2200 may be implemented by computer program instructions.

Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.

Modifications, additions, or omissions may be made to the methods described herein without departing from the scope of the disclosure. For example, the steps may be combined, modified, or deleted where appropriate, and additional steps may be added. Additionally, the steps may be performed in any suitable order without departing from the scope of the present disclosure.

Although the present disclosure has been described with several embodiments, a myriad of changes, variations, alterations, transformations, and modifications may be suggested to one skilled in the art, and it is intended that the present disclosure encompass such changes, variations, alterations, transformations, and modifications as fall within the scope of the appended claims. Therefore, the present disclosure is well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular embodiments disclosed above are illustrative only, as the present disclosure may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. It is therefore evident that the particular illustrative embodiments disclosed above may be altered or modified and all such variations are considered within the scope and spirit of the present disclosure. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. The indefinite articles “a” or “an,” as used in the claims, are each defined herein to mean one or more than one of the element that it introduces. Although certain steps in the claims include alphabetical labels (e.g., (a), (b), (c)), these are for reference only and in no way are intended to require the steps to be performed in a certain order, exclude other steps, or require the steps of the claims to be performed in a non-overlapping manner.

A number of examples have been described. Nevertheless, it will be understood that various modifications can be made. Accordingly, other implementations are within the scope of the following claims.

Claims

1. A method of forecasting production using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, comprising: (a) receiving sensor feedback from the well during well production;(b) receiving a flowrate of oil, water, and gas for the well;(c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate;(d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure;(e) estimating well deliverability with vertical lift of the well, based at least in part on one or more artificial lift parameters;(f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well;(g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well; and(h) producing fluids from the well based, at least in part, on the estimated liquid, gas, water, and oil production of the well.

2. The method of claim 1, further comprising iteratively repeating steps (c)-(g) using a cumulative flowrate of the estimated liquid production of step (g) for the cumulative liquid rate of step (c) to generate a multiphase forecast of a plurality of values of the estimated liquid, gas, water, and oil production of the well over a pre-selected time period; and producing the fluids from the well based, at least in part, on the multiphase forecast.

3. The method of claim 2, further comprising: performing an iterative process of steps (a)-(g) a second time for the well using a different one or more artificial lift parameters in step (e);comparing the multiphase forecasts resulting from the first iterative process of steps (a)-(g) using the one or more artificial lift parameters and second iterative process of steps (a)-(g) using the different one or more artificial lift parameters;selecting an artificial lift operation method based on a comparison of the multiphase forecasts; andproducing the fluids from the well using the selected artificial lift operation method.

4. The method of claim 1, wherein step (g) comprises: calculating a transient PI of the well based on the estimated BHP;computing a plurality of PVT properties for the well at the average reservoir pressure based on the sensor feedback from the well; andconducting the multiphase forecast of the estimated liquid, gas, water, and oil production of the well, based on the transient PI and the plurality of PVT properties.

5. The method of claim 4, wherein the PVT properties comprise at least solution gas-oil ratio, gas formation volume factor, oil formation volume factor, and water formation volume factor.

6. The method of claim 4, wherein step (g) further comprises: estimating a cumulative gas-oil ratio (GOR) based on the plurality of PVT properties; andconducting the multiphase forecast of the estimated liquid, gas, water, and oil production of the well, based on the transient PI and the cumulative GOR.

7. The method of claim 1, wherein step (f) further comprises: using a nodal analysis of the estimated reservoir deliverability and the estimated well deliverability to estimate the BHP of the well.

8. The method of claim 1, wherein the one or more artificial lift parameters include a plurality of artificial lift types, the plurality of artificial lift types selected from the group consisting of: a beam pump, a jet pump, a progressive cavity pump (PCP), a hydraulic pump, a gas lift, a sucker rod pump (SRP), an electric submersible pump (ESP), a plunger lift, and a gas-assisted plunger lift (GAPL), and any combination of thereof.

9. The method of claim 7, further comprising: determining a best type of artificial lift using one or more parameters selected from the group consisting of: target liquid rate, highest predicted gas-liquid ratio (GLR), influence of dog leg severity (DLS) during operation, anticipated line pressure, downhole temperature, and any combination thereof.

10. A system for forecasting production using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, comprising: one or more processors; andone or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components of the system to perform operations comprising:(a) receiving sensor feedback from the well during well production;(b) receiving a flowrate of oil, water, and gas for the well;(c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate;(d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure;(e) estimating well deliverability with vertical lift of the well, based at least in part on one or more artificial lift parameters;(f) estimating a bottomhole pressure (BHP) of the well based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability;(g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well; and(h) generate a production plan for fluids from the well based, at least in part, on the estimated liquid, gas, water, and oil production of the well.

11. The system of claim 10, the operations further comprising iteratively repeating steps (c)-(g) using a cumulative flowrate of the estimated liquid production of step (g) for the cumulative liquid rate of step (c) to provide a multiphase forecast of a plurality of values of the estimated liquid, gas, water, and oil production of the well over a pre-selected time period; and generating the production plan for producing the fluids from the well based, at least in part, on the multiphase forecast.

12. The system of claim 11, the operations further comprising: performing an iterative process of steps (a)-(g) a second time for the well using a different one or more artificial lift parameters in step (e);comparing the multiphase forecasts resulting from the first iterative process of steps (a)-(g) using the one or more artificial lift parameters and second iterative process of steps (a)-(g) using the different one or more artificial lift parameters;selecting an artificial lift operation method based, at least in part, on a comparison of the multiphase forecasts; andgenerating the production plan for producing the fluids from the well using the selected artificial lift operation method.

13. The system of claim 10, wherein step (g) comprises: calculating a transient PI of the well based, at least in part, on the estimated BHP;computing a plurality of PVT properties for the well at the average reservoir pressure based, at least in part, on the sensor feedback from the well; andgenerating the multiphase forecast of the estimated liquid, gas, water, and oil production of the well, based on the transient PI and the plurality of PVT properties.

14. The system of claim 13, wherein the PVT properties comprise at least solution gas-oil ratio, gas formation volume factor, oil formation volume factor, and water formation volume factor.

15. The system of claim 13, wherein step (g) further comprises: estimating a cumulative gas-oil ratio (GOR) based, at least in part, on the plurality of PVT properties; andgenerating the multiphase forecast of the estimated liquid, gas, water, and oil production of the well, based on the transient PI and the cumulative GOR.

16. The system of claim 10, wherein the step (f) further comprises: using a nodal analysis to estimate the BHP of the well based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability.

17. The system of claim 16, wherein the one or more artificial lift parameters comprise a plurality of artificial lift types, the plurality of artificial lift types selected from the group consisting of: a beam pump, a jet pump, a progressive cavity pump (PCP), a hydraulic pump, a gas lift, a sucker rod pump (SRP), an electric submersible pump (ESP), a plunger lift, a gas-assisted plunger lift (GAPL), and any combination thereof.

18. The system of claim 16, further comprising: determining a best type of artificial lift using one or more parameters selected from the group consisting of: target liquid rate, highest predicted gas-liquid ratio (GLR), influence of dog leg severity (DLS) during operation, anticipated line pressure, downhole temperature, and any combination thereof.

19. A method of producing fluids using an artificial lift operation in a well penetrating a reservoir in a subterranean formation, comprising: (a) receiving sensor feedback from the well during well production;(b) receiving a flowrate of oil, gas, and water for the well;(c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate;(d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure;(e) estimating well deliverability with vertical lift of the well, based at least in part on one or more first artificial lift parameters for first artificial lift equipment;(f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well;(g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well;(h) performing steps (a)-(g) a second time for the well using one or more second artificial lift parameters for second artificial lift equipment;(i) comparing the multiphase forecasts resulting from the first process of steps (a)-(g) using the one or more first artificial lift parameters and second process of steps (a)-(g) using the one or more second artificial lift parameters;(j) selecting artificial lift equipment to be used in the artificial lift operation based on a comparison of the multiphase forecasts; and(k) producing the fluids from the well using the selected artificial lift equipment.

20. The method of claim 19, wherein the second artificial lift equipment comprises a different type of artificial lift equipment than the first lift equipment, or a same type of artificial lift equipment having different operational properties.

21. The method of claim 19, wherein: the step (g) further comprises iteratively repeating steps (c)-(g) using a cumulative flowrate of the estimated liquid production of step (g) for the cumulative liquid rate of step (c) to provide a multiphase forecast of a plurality of values of the estimated liquid, gas, water, and oil production of the well over a pre-selected time period, andthe one or more artificial lift parameters for the second artificial lift equipment in step (h) being indicative of artificial lift equipment being used during a different portion of the pre-selected time period.

22. A method of producing fluids using gas lift injection equipment in multiple wells of a well pad, each well extending through a subterranean formation, comprising: for each well of the multiple wells: (a) receiving sensor feedback from the well during well production;(b) receiving a flowrate of oil, gas, and water for the well;(c) calculating an average reservoir pressure and a productivity index (PI) of the well based on a cumulative liquid rate;(d) estimating a reservoir deliverability of the reservoir based on the PI and the average reservoir pressure;(e) estimating well deliverability with vertical lift of the well, based at least in part on one or more gas lift injection parameters;(f) estimating based, at least in part, on the estimated reservoir deliverability and the estimated well deliverability to estimate a bottomhole pressure (BHP) of the well;(g) generating a multiphase forecast of an estimated liquid, gas, water, and oil production of the well, based, at least in part, on the estimated BHP and the sensor feedback from the well;(h) generating a gas lift performance (GLP) curve for the well based, at least in part, on the estimated oil production of the well;comparing the GLP curves of the multiple wells; andadjusting amounts of pressure output from a compressor to the gas lift injection equipment at two or more of the multiple wells based, at least in part, on the comparison of the GLP curves.