Patent Application
20240402383
The present disclosure relates generally to the field of controlling well operations in unconventional reservoirs using a hybrid modeling approach.
Some unconventional wells (wells in unconventional reservoirs) may experience fast production rate decline and may experience quick reservoir depletion with ensuing rapid pressure drop. Understanding dynamic behavior of unconventional wells is critical for efficient operation of unconventional wells.
This disclosure relates to controlling well operations in unconventional reservoirs. Operation information for a well in an unconventional reservoir, production information for the well, and/or other information may be obtained. The operation information may define operation characteristics of the well. The production information may define production characteristics of the well. Historical flowing bottom hole pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. Forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well and/or other information. Forecasted production characteristics of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information.
Forecasted drawdown pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. Forecasted gas/oil ratio of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. Production parameters for the well may be determined based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well, the forecasted drawdown pressure of the well, the forecasted gas/oil ratio of the well, and/or other information. The production parameters for the well may include shut-in bottom hole pressure that serves as proxy for reservoir pressure in the unconventional reservoir. One or more operations of the well may be facilitated based on the production parameters for the well and/or other information.
A system for controlling well operations in unconventional reservoirs may include one or more electronic storage, one or more processors and/or other components. The electronic storage may store information relating to an unconventional reservoir, information relating to a well, information relating to a field, operation information, information relating to operation characteristics of a well, production information, information relating to production characteristics of a well, information relating to historical flowing bottom hole pressure of a well, information relating to forecasted flowing bottom hole pressure of a well, information relating to forecasted production characteristics of a well, information relating to forecasted gas/oil ratio of a well, information relating to production parameters for a well, information relating to operations of a well, and/or other information.
The processor(s) may be configured by machine-readable instructions. Executing the machine-readable instructions may cause the processor(s) to facilitate controlling well operations in unconventional reservoirs. The machine-readable instructions may include one or more computer program components. The computer program components may include one or more of an operation information component, a production information component, a historical flowing bottom hole pressure component, a forecasted flowing bottom hole pressure component, a forecasted production characteristics component, a forecasted drawdown pressure component, a forecasted gas/oil ratio component, a production parameters component, an operation component, and/or other computer program components.
The operation information component may be configured to obtain operation information for a well in an unconventional reservoir and/or other information. The operation information may define operation characteristics of the well.
The production information component may be configured to obtain production information for the well and/or other information. The production information may define production characteristics of the well.
The historical flowing bottom hole pressure component may be configured to determine historical flowing bottom hole pressure of the well. The historical flowing bottom hole pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. In some implementations, the historical flowing bottom hole pressure of the well may be determined based on the operation characteristics of the well and/or the production characteristics of the well by inputting the operation characteristics of the well and/or the production characteristics of the well into one or more machine learning model. The machine learning model(s) may be configured to output the historical flowing bottom hole pressure of the well. In some implementations, the machine learning model(s) may be trained based on fluid flow simulation of the well and/or other information.
The forecasted flowing bottom hole pressure component may be configured to determine forecasted flowing bottom hole pressure of the well. The forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well and/or other information. In some implementations, the forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well using an exponential decline analysis and/or other analyses.
The forecasted production characteristics component may be configured to determine forecasted production characteristics of the well. The forecasted production characteristics of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. In some implementations, the forecasted production characteristics of the well may be determined based on the production characteristics of the well using a decline curve analysis and/or other analyses.
The forecasted drawdown pressure component may be configured to determine forecasted drawdown pressure of the well. The forecasted drawdown pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. In some implementations, the forecasted drawdown pressure of the well may be determined based on the operation characteristics of the well and/or the production characteristics of the well by inputting the operation characteristics of the well and/or the production characteristics of the well into one or more machine learning models. The machine learning model(s) may be configured to output the forecasted drawdown pressure of the well. In some implementations, the machine learning model may be trained based on shut-in simulation of the well and/or other information. In some implementations, the shut-in simulation of the well may simulate shut-in of the well for a single day and/or other time durations.
The forecasted gas/oil ratio component may be configured to determine forecasted gas/oil ratio of the well. The forecasted gas/oil ratio of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information.
The production parameters component may be configured to determine production parameters for the well. The production parameters for the well may be determined based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well, the forecasted drawdown pressure of the well, the forecasted gas/oil ratio of the well, and/or other information. The production parameters for the well may include shut-in bottom hole pressure that serves as proxy for reservoir pressure in the unconventional reservoir. In some implementations, the production parameters for the well may further include water cut and productivity index.
The operation component may be configured to facilitate one or more operations of the well. The operation(s) of the well may be facilitated based on the production parameters for the well and/or other information. In some implementations, facilitation of the operation(s) of the well based on the production parameters for the well may include determination of artificial lift parameters for the well based on the production parameters for the well and/or other information.
These and other objects, features, and characteristics of the system and/or method disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
The present disclosure relates to controlling well operations in unconventional reservoirs. A hybrid modeling approach incorporates both physics-based reservoir modeling and machine learning technique to capture dynamic behavior of unconventional wells. Shut-in bottom hole pressure for unconventional wells are simulated for use as proxy for reservoir pressure in unconventional reservoirs. Production parameters for unconventional wells (e.g., gas/oil ratio, water cut, flowing bottom hole pressure, shut-in bottom hole pressure, productivity index) are determined for use in controlling the operations of unconventional wells.
The methods and systems of the present disclosure may be implemented by a system and/or in a system, such as a system 10 shown in
Forecasted drawdown pressure of the well may be determined by the processor 11 based on the operation characteristics of the well, the production characteristics of the well, and/or other information. Forecasted gas/oil ratio of the well may be determined by the processor 11 based on the operation characteristics of the well, the production characteristics of the well, and/or other information. Production parameters for the well may be determined by the processor 11 based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well, the forecasted drawdown pressure of the well, the forecasted gas/oil ratio of the well, and/or other information. The production parameters for the well may include shut-in bottom hole pressure that serves as proxy for reservoir pressure in the unconventional reservoir. One or more operations of the well may be facilitated by the processor 11 based on the production parameters for the well and/or other information.
The electronic storage 13 may be configured to include one or more electronic storage media that electronically stores information. The electronic storage 13 may store software algorithms, information determined by the processor 11, information received remotely, and/or other information that enables the system 10 to function properly. For example, the electronic storage 13 may store information relating to an unconventional reservoir, information relating to a well, information relating to a field, operation information, information relating to operation characteristics of a well, production information, information relating to production characteristics of a well, information relating to historical flowing bottom hole pressure of a well, information relating to forecasted flowing bottom hole pressure of a well, information relating to forecasted production characteristics of a well, information relating to forecasted gas/oil ratio of a well, information relating to production parameters for a well, information relating to operations of a well, and/or other information.
The electronic display 14 may refer to an electronic device that provides visual presentation of information. The electronic display 14 may include a color display and/or a non-color display. The electronic display 14 may be configured to visually present information. The electronic display 14 may present information using/within one or more graphical user interfaces. For example, the electronic display 14 may present information relating to an unconventional reservoir, information relating to a well, information relating to a field, operation information, information relating to operation characteristics of a well, production information, information relating to production characteristics of a well, information relating to historical flowing bottom hole pressure of a well, information relating to forecasted flowing bottom hole pressure of a well, information relating to forecasted production characteristics of a well, information relating to forecasted gas/oil ratio of a well, information relating to production parameters for a well, information relating to operations of a well, and/or other information.
Dynamic behavior of unconventional reservoir makes operations of wells in the unconventional reservoir (unconventional wells) difficult. For example, inflow performance relationship (IPR) is a reservoir performance indicator that correlates flowing bottomhole pressure with production rate of a well. Inflow performance relationship may include a curve of production rate plotted against flowing bottomhole pressure for different types of wells (e.g., oil wells, water wells, gas wells). The shape of the curve may be influenced by the reservoir fluid composition, reservoir properties, wellbore conditions, near wellbore formation conditions, and/or other factors. The combination of an outflow performance curve and an inflow performance curve for a well may be used in nodal analysis to determine performance of the well (e.g., in producing fluid) and/or to operate the well (e.g., in increasing production, minimizing formation damage such as plugging of pore throats from fines migration or proppant embedment).
Inflow performance relationship of unconventional wells may be a critical factor/information for modeling behavior of unconventional wells and/or unconventional reservoir. Inflow performance relationship of unconventional wells may be a critical factor/information for improving reservoir management and/or artificial lift through production optimization. Inflow performance relationship may reflect the reservoir behavior during production and may be critical for production optimization and drawdown management. Some unconventional wells/reservoirs may experience fast production rate decline and may experience quick reservoir depletion with the ensuing rapid pressure drop. Unlike the conventional wells/reservoirs, unconventional wells/reservoirs decline behavior is not clearly understood from the first-principle perspective. It is quite challenging in terms of time and effort to capture each well's IPR with conventional reservoir simulation method/process, especially as the number of wells increases.
One of the biggest uncertainties in understanding behavior of unconventional wells/reservoirs is the shut-in bottom hole pressure (SBHP) that corresponds to the wellbore pressure at zero flow rate. Existing solutions may utilize an arbitrary combination of an estimated SBHP and productivity index (PI) with certain constraints (e.g., prior shut-in pressure values) to match the well test through nodal analysis. By nature, this approach accepts the uncertainty with certain assumptions. But such approximation of unconventional well/reservoir behavior results in sub-optimal operational recommendations (e.g., inaccurate/inefficient gas-lift allocation/suboptimal artificial lift design).
The present disclosure provides a tool to accurately model dynamic behavior of unconventional wells/reservoirs and determine production parameters to improve (e.g., increase efficiency of, optimize) well operations. A hybrid modeling that incorporates both physics-based modeling and machine-learning techniques is used to capture the dynamic behavior of unconventional wells/reservoirs. The tool enables behavior of unconventional wells/reservoirs, such as inflow performance relationship, flowing bottom hole pressure, productivity index, drawdown pressure, gas/oil ratio, and/or water cut, to be accurately determined (e.g., estimated, calculated, predicted, forecasted).
Historical data and forecasted data of well(s) may be obtained. For example, historical oil rate, water rate, gas rate, and/or flowing bottom hole pressure of a well may be obtained. Historical oil rate, water rate, gas rate, and/or flowing bottom hole pressure of a well may be used to forecast oil rate, water rate, gas rate, and/or flowing bottom hole pressure of the well in the future.
In some implementations, multiple wells may be selected from an unconventional reservoir. Oil rate, water rate, gas rate, and flowing bottom hole pressure of the selected wells may be obtained. The multiple wells may be selected to be representative of the unconventional reservoir. For example, P10, P50, P90 wells from different areas of the unconventional reservoir may be selected to be representative of the unconventional reservoir. The wells may be selected based on well performance (e.g., production), target benches, subsurface production zones, geographical locations, and/or other factors.
At step 304, shut-in bottom hole pressure of well(s) may be simulated. Reservoir pressure in an unconventional reservoir may be difficult to obtain because of the low permeability the unconventional reservoir. Shut-in bottom hole pressure may be simulated for use as proxy for reservoir pressure in the unconventional reservoir. Shut-in bottom hole pressure may be simulated using a physics-based reservoir simulator. Shut-in bottom hole pressure may refer to the pressure at the bottom of the well (wellbore) when the well is closed (e.g., when the surface valves of the well are closed). The well(s) and/or the unconventional reservoir may be modeled using information relating to well(s) (e.g., operation characteristics, production characteristics). Shut-in bottom hole pressure may be simulated (e.g., in fluid flow simulation) by setting the flow rate of a well to zero.
Shut-in bottom hole pressure may be simulated for different durations of time. For example, a single-day, multiple days, a single-week, multiple weeks, a single-month, multiple months of a well shut-in may be simulated. Shut-in of wells may be simulated at different times. For example, shut-in of wells may be simulated at different periodic intervals. For instance, shut-in of wells may be simulated daily, weekly, monthly, quarterly, and/or at other periodic intervals.
Shut-in bottom hole pressure may be simulated using historical data and forecasted data. For example, historical shut-in bottom hole pressure of a well at different times in the past may be simulated using historical data for the well. Forecasted shut-in bottom hole pressure of the well at different times in the future may be simulated using forecasted data for the well.
At step 306, drawdown pressure of well(s) may be determined. Drawdown pressure may refer to the difference between the reservoir pressure and the flowing bottom hole pressure. Drawdown pressure may refer to the pressure that drives fluids from a reservoir into a well. Shut-in bottom hole pressure may be used as proxy for reservoir pressure, and the drawdown pressure (DP) may be calculated as the difference between the shut-in bottom hole pressure (SBHP) and the flowing bottom hole pressure (FBHP). Drawdown pressure may be determined for different times in the past using historical data and for different times in the future using forecasted data:
DP=SBHP−FBHP
Use of physics-based simulations to model reservoir behavior may be difficult and resource intensive/computationally expensive. It may be impractical to perform simulations of wells/reservoir to obtain flowing bottom hole pressure, shut-in bottom hole pressure, and/or drawdown pressure of wells. Information obtained from flow diagram 300 may be used to train machine-learning models to facilitate control of well operations. For example, machine-learning models may be trained to output flowing bottom hole pressure, drawdown pressure, and/or other characteristics of unconventional wells/reservoir. The machine-learning models may be used to increase the speed of unconventional wells/reservoir behavior analysis while decreasing the cost of unconventional wells/reservoir behavior analysis. The machine learning models may include one or multiple types of model or utilize one or multiple types of algorithms. Example types of machine-learning models or algorithms that may be used include random forest, XGBoost, AdaBoost, lasso, long short-term memory, and ridge. Use of other types of machine-learning model is contemplated.
Operation characteristics of a well may refer to attribute, quality, configuration, parameter, and/or characteristics of matter inside, within, and/or around the well during operation. Operation characteristics of a well may refer to characteristics of the well, characteristics of one or more components of the well, characteristics of conditions around the well, characteristics of conditions inside the well, and/or other characteristics of the during operation. Operation characteristic of a well may include static characteristics (e.g., design of the well) and/or dynamic characteristics (e.g., properties of materials/fluids inside or around the well, operation parameters of the well). Operation characteristic of a well may be measured using one or more sensors. For example, operation characteristics of a well may include flowing bottom hole pressure, wellhead pressure, downhole pressure, casing head pressure/gas injection pressure, flowrate, temperature, well depth, true vertical depth, well location, and/or reservoir thickness. Other types of operation characteristics are contemplated.
Production characteristics of a well may refer to attribute, quality, configuration, parameter, and/or characteristics of matter produced, extracted, and/or recovered from/through the well during operation. Production characteristics of a well may refer to characteristics of matter inside and/or around the well during operation. Production characteristics of a well may include static characteristics and/or dynamic characteristics. Production characteristic of a well may be measured using one or more sensors. For example, production characteristics of a well may include oil rate (oil production rate), water rate (water production rate), gas rate (gas production rate), gas/oil ratio, cumulative production (e.g., cumulative oil production, cumulative water production, cumulative gas production). Other types of production characteristics are contemplated.
In some implementations, one or more pre-processing techniques may be utilized on the input data 410. For example, the input data 410 may be preprocessed as part of data quality check, to remove outlier data, and/or to otherwise prepare the input data 410 for use. For example, the input data 410 may be preprocessed to remove outliers, anomalies, unreasonable values (e.g., according to subject matter experts), zero values, not applicable values, text entries, and/or other values. Curve smoothing (e.g., Hanning window smoothing) may be performed to clean the input data 410. Long short-term memory (LSTM) autoencoder may be used to remove outliers and/or anomalies from time series data.
The input data 410 (the operation characteristics 412 and/or the production characteristics 414) may be used to determine one or more characteristics of the well(s) in the unconventional reservoir. For example, at step 422, the input data 410 may be used to determine historical flowing bottom hole pressure of the well(s). The input data 410 may be input into one or more machine learning models trained to output flowing bottom hole pressure. For example, the machine learning model(s) may be trained to receive as input the operation characteristics 412 and/or the production characteristics 414 of a well corresponding to particular moment(s) in time, and output the flowing bottom hole pressure of the well at the particular moment(s) in time. Historical input data may be input into the machine learning model(s) to obtain historical flowing bottom hole pressure of the well. Present input data may be input into the machine learning model(s) to obtain present flowing bottom hole pressure of the well. Forecasted input data may be input into the machine learning model(s) to obtain forecasted flowing bottom hole pressure of the well. At step 422, historical input data may be input into the machine learning model(s) to obtain historical flowing bottom hole pressure of the well(s).
The machine learning model(s) may be trained based on fluid flow simulation of the well(s) and/or other information. For example, a physics-based modeling tool may be used to perform fluid flow simulation of a well, and convert operation characteristics and/or production characteristics of the well into the flowing bottomhole pressure of the well. The inputs (e.g., operation characteristics, production characteristics) and outputs (e.g., flowing bottom hole pressure) of the conversion using the physics-based modeling may be used as input-output pairs for training the machine-learning model(s).
At step 424, forecasted flowing bottom hole pressure of well(s) may be determined. Forecasted flowing bottom hole pressure of a well may refer to flowing bottom hole pressure of the well that is predicted/estimated to occur in the future. Forecasted flowing bottom hole pressure of a well may be determined for multiple moments in the future based on the historical flowing bottom hole pressure of the well and/or other information. For example, forecasted flowing bottom hole pressure of a well may be determined using analytical equation(s) to extrapolate flowing bottom hole pressure in the future based on historical flow bottom hole pressure. For example, time-series of forecasted flowing bottom hole pressure of a well may be determined based on time-series of historical flowing bottom hole pressure of the well using an exponential decline analysis. Use of other forecasting techniques is contemplated.
At step 426, the input data 410 (the operation characteristics 412 and/or the production characteristics 414) may be used to determine forecasted production characteristics of the well(s). Forecasted production characteristics of a well may refer to production characteristics of the well that is predicted/estimated to occur in the future. Forecasted production characteristics of a well may be determined for multiple moments in the future based on the historical operation characteristics of the well, forecasted operation characteristics of the well, historical production characteristics of the well, and/or other information. For example, the forecasted production characteristics may be determined using curve fitting analysis to extrapolate production characteristics in the future based on historical production characteristics. For example, time-series of forecasted production characteristics of a well may be determined based on time-series of historical production characteristics of the well using a decline-curve analysis. For instance, time-series of forecasted oil rate and forecasted water rate of a well may be determined based on time-series of historical oil rate and historical water rate of the well using a decline-curve analysis. Separate analysis may be performed for separate production characteristics of the wells. Use of other forecasting techniques is contemplated.
At step 428, the input data 410 may be used to determine drawdown pressure of the well(s). The input data 410 may be input into one or more machine learning models trained to output drawdown pressure. For example, the machine learning model(s) may be trained to receive as input the operation characteristics 412 and/or the production characteristics 414 of a well corresponding to particular moment(s) in time, and output the drawdown pressure of the well at the particular moment(s) in time. Historical input data may be input into the machine learning model(s) to obtain historical drawdown pressure of the well. Present input data may be input into the machine learning model(s) to obtain present drawdown pressure of the well. Forecasted input data may be input into the machine learning model(s) to obtain forecasted drawdown pressure of the well. At step 428, historical/present/forecasted input data may be input into the machine learning model(s) to obtain historical/present/forecasted drawdown pressure of the well(s).
The machine learning model(s) may be trained based on shut-in simulation of the well(s) and/or other information. For example, a physics-based modeling tool may be used to perform fluid flow simulation of a well, and shut-in of the well may be simulated for a single hour, multiple hours, a single-day (e.g., 24 hours), multiple days, a single-week, multiple weeks, a single-month, multiple months, and/or other durations of times. Simulated shut-in of the well may be repeated at periodic intervals, such as daily, weekly, monthly, quarterly, and/or at other periodic intervals. Shut-in simulation of the well may provide shut-in bottom hole pressure of the well. The drawdown pressure of the well may be determined by calculating the difference between the shut-in bottom hole pressure and the flowing bottom hole pressure. The inputs (e.g., operation characteristics, production characteristics) and outputs (e.g., drawdown pressure) of the physics-based modeling may be used as input-output pairs for training the machine-learning model(s).
At step 430, the input data 410 may be used to determine gas/oil ratio of the well(s). The input data 410 may be input into one or more machine learning models trained to output gas/oil ratio. For example, the machine learning model(s) may be trained to receive as input the operation characteristics 412 and/or the production characteristics 414 of a well corresponding to particular moment(s) in time, and output the gas/oil ratio of the well at the particular moment(s) in time. Historical input data may be input into the machine learning model(s) to obtain historical gas/oil ratio of the well. Present input data may be input into the machine learning model(s) to obtain present gas/oil ratio of the well. Forecasted input data may be input into the machine learning model(s) to obtain forecasted gas/oil ratio of the well. At step 430, historical/present/forecasted input data may be input into the machine learning model(s) to obtain historical/present/forecasted gas/oil ratio of the well(s).
The machine learning model(s) may be trained based on fluid flow simulation of the well(s) and/or other information. For example, a physics-based modeling tool may be used to perform fluid flow simulation of a well, and convert operation characteristics and/or production characteristics of the well into the gas/oil ratio of the well. The inputs (e.g., operation characteristics, production characteristics) and outputs (e.g., gas/oil ratio) of the conversion using the physics-based modeling may be used as input-output pairs for training the machine-learning model(s).
By the end of the flow diagram 400, the following information for the well(s) may be obtained: historical and forecasted flowing bottom hole pressure, historical and forecasted oil rate and water rate, historical and forecasted drawdown pressure, and historical and forecasted gas/oil ratio. Trends of flowing bottom hole pressure, oil rate, water rate, drawdown pressure, and gas/oil ratio may be obtained. Other information for the well(s) may be obtained by the end of the flow diagram 400.
The inputs and/or the outputs of the calculation 510 may be used as production parameters 520 for the well(s). For example, the gas/oil ratio, the water cut, the flowing bottom hole pressure, the shut-in bottom hole pressure, and the productivity index may be used as the production parameters 520 for the well(s). The production parameters 520 of the well(s) may be used to facilitate one or more operations at the well(s). For example, the production parameters 520 of the well(s) may be used to start, stop, modify, and/or otherwise control operations of the well(s). The production parameters 520 of the well(s) may be used to determine the inflow performance relationship(s) of the well(s). The operations at the well(s) may be performed using the production parameters 520 to improve (e.g., increase efficiency of, optimize) well operations.
Referring back to
The operation information component 102 may be configured to obtain operation information for a well in an unconventional reservoir and/or other information. Obtaining operation information may include one or more of accessing, acquiring, analyzing, determining, developing, examining, generating, identifying, loading, locating, opening, preparing, receiving, retrieving, reviewing, selecting, storing, and/or otherwise obtaining the operation information. The operation information component 102 may obtain operation information from one or more locations. For example, the operation information component 102 may obtain operation information from a storage location, such as the electronic storage 13, electronic storage of a device accessible via a network, and/or other locations. The operation information component 102 may obtain operation information from one or more hardware components (e.g., a computing device) and/or one or more software components (e.g., software running on a computing device).
A well may refer to a hole that is drilled in the ground. A well may be drilled in the ground for exploration and/or recovery of resources in the ground, such as water or hydrocarbons. For example, a well may be drilled for production of hydrocarbons (e.g., as a production well). The term “wellbore,” “well bore,” “borehole,” and the like may be utilized interchangeably with the term “well.”
A reservoir may refer to a location at which one or more resources are stored. For example, a reservoir may refer to a location at which hydrocarbons are stored. For instance, a reservoir may refer to a location including rocks in which oil and/or natural gas have accumulated. A reservoir may include regions above the surface, at the surface, and/or below the surface. For example, a reservoir may include one or more subsurface regions. A subsurface region may refer to a part of earth located beneath the surface/located underground. A subsurface region may refer to a part of earth that is not exposed at the surface of the ground. A reservoir may include one or more wells. For example, a reservoir may include one or more injection wells (e.g., for injection of fluid), one or more production wells (e.g., for extraction of oil or gas), and/or other wells. A reservoir may refer to a location in which buoyant forces keep hydrocarbons in place below a sealing caprock. A reservoir may refer to a location in which oil or natural gas do not readily flow into a well. A reservoir may refer to a location in which hydraulic fractures may be used to extract the stored resources, such as an unconventional reservoir (e.g., tight-sand, gas and/or oil shales). An unconventional reservoir may refer to a reservoir where hydrocarbons and/or other resources (e.g., oil, gas) are tightly bound to the rock fabric by strong capillary forces. Resources may be held by dense structure with lower permeability.
The term “formation,” “subsurface formation,” “subterranean formation,” “subsurface volume of interest,” “subsurface region of interest,” and the like may be utilized interchangeably with the term “reservoir.” In some implementations, an unconventional reservoir may have a permeability of less than 25 millidarcy (mD) (e.g., 20 mD or less, 15 mD or less, 10 mD or less, 5 mD or less, 1 mD or less, 0.5 mD or less, 0.1 mD or less, 0.05 mD or less, 0.01 mD or less, 0.005 mD or less, 0.001 mD or less, 0.0005 mD or less, 0.0001 mD or less, 0.00005 mD or less, 0.00001 mD or less, 0.000005 mD or less, 0.000001 mD or less, or less). In some implementations, a reservoir may have a permeability of at least 0.000001 mD (e.g., at least 0.000005 mD, at least 0.00001 mD, 0.00005 mD, at least 0.0001 mD, 0.0005 mD, 0.001 mD, at least 0.005 mD, at least 0.01 mD, at least 0.05 mD, at least 0.1 mD, at least 0.5 mD, at least 1 mD, at least 5 mD, at least 10 mD, at least 15 mD, or at least 20 mD). An unconventional reservoir may have a permeability ranging from any of the minimum values described above to any of the maximum values described above. For example, in some implementations, an unconventional reservoir may have a permeability of from 0.000001 mD to 25 mD (e.g., from 0.001 mD to 25 mD, from 0.001 mD to 10 mD, from 0.01 mD to 10 mD, from 0.1 mD to 10 mD, from 0.001 mD to 5 mD, from 0.01 mD to 5 mD, or from 0.1 mD to 5 mD).
The operation information may define operation characteristics of the well in the unconventional reservoir. Operation characteristics of a well may refer to attribute, quality, configuration, parameter, and/or characteristics of matter inside, within, and/or around the well during operation. Operation characteristics of a well may refer to characteristics of the well, characteristics of one or more components of the well, characteristics of conditions around the well, characteristics of conditions inside the well, and/or other characteristics of the during operation. Operation characteristic of a well may include static characteristics (e.g., design of the well) and/or dynamic characteristics (e.g., properties of materials/fluids inside or around the well, operation parameters of the well). Operation characteristic of a well may be measured using one or more sensors. For example, operation characteristics of a well may include flowing bottom hole pressure, wellhead pressure, downhole pressure, casing head pressure/gas injection pressure, flowrate, temperature, well depth, true vertical depth, well location, and/or reservoir thickness. Other types of operation characteristics are contemplated.
The operation information may define operation characteristics of a well by including information that characterizes, describes, delineates, identifies, is associated with, quantifies, reflects, sets forth, and/or otherwise defines one or more of value, property, quality, quantity, attribute, feature, and/or other aspects of the operation characteristics of the well. The operation information may directly and/or indirectly define operation characteristics of a well. For example, the operation information may define operation characteristics of a well by including information that specifies the type and/or value of the operation characteristics of the well and/or information that may be used to determine the type and/or value of the operation characteristics of the well. Other types of operation information are contemplated.
The operation information may define operation characteristics of a well at different moments. The operation information may define operation characteristics of a well at different moments in the past. The operation information may include time-series data of the operation characteristics of the well.
The production information component 104 may be configured to obtain production information for the well in the unconventional reservoir and/or other information. Obtaining production information may include one or more of accessing, acquiring, analyzing, determining, developing, examining, generating, identifying, loading, locating, opening, preparing, receiving, retrieving, reviewing, selecting, storing, and/or otherwise obtaining the production information. The production information component 104 may obtain production information from one or more locations. For example, the production information component 104 may obtain production information from a storage location, such as the electronic storage 13, electronic storage of a device accessible via a network, and/or other locations. The production information component 104 may obtain production information from one or more hardware components (e.g., a computing device) and/or one or more software components (e.g., software running on a computing device).
The production information may define production characteristics of the well in the unconventional reservoir. Production characteristics of a well may refer to attribute, quality, configuration, parameter, and/or characteristics of matter produced, extracted, and/or recovered from/through the well during operation. Production characteristics of a well may refer to characteristics of matter inside and/or around the well during operation. Production characteristics of a well may include static characteristics and/or dynamic characteristics. Production characteristic of a well may be measured using one or more sensors. For example, production characteristics of a well may include oil rate (oil production rate), water rate (water production rate), gas rate (gas production rate), gas/oil ratio, cumulative production (e.g., cumulative oil production, cumulative water production, cumulative gas production). Other types of production characteristics are contemplated.
The production information may define production characteristics of a well by including information that characterizes, describes, delineates, identifies, is associated with, quantifies, reflects, sets forth, and/or otherwise defines one or more of value, property, quality, quantity, attribute, feature, and/or other aspects of the production characteristics of the well. The production information may directly and/or indirectly define production characteristics of a well. For example, the production information may define production characteristics of a well by including information that specifies the type and/or value of the production characteristics of the well and/or information that may be used to determine the type and/or value of the production characteristics of the well. Other types of production information are contemplated.
The production information may define production characteristics of a well at different moments. The production information may define production characteristics of a well at different moments in the past. The production information may include time-series data of the production characteristics of the well.
The historical flowing bottom hole pressure component 106 may be configured to determine historical flowing bottom hole pressure of the well. Determining historical flowing bottom hole pressure may include ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, performing, quantifying, and/or otherwise determining the historical flowing bottom hole pressure. Determining historical flowing bottom hole pressure may include determining values of flowing bottom hole pressure at one or more moments in the past. Determining historical flowing bottom hole pressure may include determining separate values of flowing bottom hole pressure at separate moments in the past.
The historical flowing bottom hole pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. The historical flowing bottom hole pressure of the well at a particular moment in the past may be determined based on the operation characteristics of the well at the particular moment in the past and/or the production characteristics of the well at the particular moment in the past. Time-series of the operation characteristics and/or the production characteristics may be used to determine times series of the historical flowing bottom hole pressure of the well.
In some implementations, the historical flowing bottom hole pressure of the well may be determined based on the operation characteristics of the well and/or the production characteristics of the well by inputting the operation characteristics of the well and/or the production characteristics of the well into one or more machine learning model. The machine learning model(s) may be configured (e.g., trained) to output the historical flowing bottom hole pressure of the well. In response to the operation characteristics of the well and/or the production characteristics of the well for a particular moment in the past being input into the machine learning model(s), the machine learning model(s) may output the historical flowing bottom hole pressure of the well at the particular moment in the past. The machine learning model(s) outputting the historical flowing bottom hole pressure of the well may include the machine learning model(s) outputting the value of the historical flowing bottom hole pressure and/or information from which the value of the historical flowing bottom hole pressure may be determined. The machine learning model(s) may output separate values of historical flowing bottom hole pressure of the well for separate moments in the past.
In some implementations, the machine learning model(s) may be trained based on fluid flow simulation of the well and/or other information. For example, the machine learning model(s) may be trained as described with respect to step 422 of
In some implementations, different machine learning models may be trained for use with different types and/or different combinations of input. For example, possible input to the machine learning model(s) for determination of historical flowing bottom hole pressure may include wellhead pressure, downhole pressure, casing head pressure, flowrate, temperature, well depth, true vertical depth, well location, reservoir thickness, oil rate, water rate, gas rate, gas/oil ratio, and cumulative production. Different machine learning models may be trained based on whether downhole pressure of the well is available or not. For example, certain wells may include a downhole pressure gauge, and downhole pressure of the well may be available. Certain wells may not include a downhole pressure gauge or the downhole pressure gauge may be down, and downhole pressure of the well may not be available. One machine learning model may be trained for input with downhole pressure and another machine learning model may be trained for input without downhole pressure. In some implementations, the machine learning model trained for input with downhole pressure may be more accurate in determination of historical flowing bottom hole pressure than the machine learning model trained for input without downhole pressure.
Different combinations of inputs may be used for different machine learning models. For example, the importance of different types of input (feature importance) may change between the machine learning model trained for input with downhole pressure and the machine learning model trained for input without downhole pressure. For example,
The forecasted flowing bottom hole pressure component 108 may be configured to determine forecasted flowing bottom hole pressure of the well. Determining forecasted flowing bottom hole pressure may include ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, performing, quantifying, and/or otherwise determining the forecasted flowing bottom hole pressure. Determining forecasted flowing bottom hole pressure may include determining values of flowing bottom hole pressure at one or more moments in the future. Determining forecasted flowing bottom hole pressure may include determining separate values of flowing bottom hole pressure at separate moments in the future.
The forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well and/or other information. The forecasted flowing bottom hole pressure of the well at multiple moments in the future may be determined based on the historical flowing bottom hole pressure of the well at multiple moments in the past. Time-series of the historical flowing bottom hole pressure of the well may be used to determine time-series of the forecasted flowing bottom hole pressure of the well.
In some implementations, the forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well using one or more analytical equations. The analytical equation(s) may be used to extrapolate values of forecasted flowing bottom hole pressure in the future based on values of historical flowing bottom hole pressure in the past. For example, the forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well using an exponential decline analysis and/or other analyses. For instance, a three-parameter exponential decline analysis may be used to fit a curve to the historical flowing bottom hole pressure and predict the forecasted flowing bottom hole pressure. The curve may be defined by the following function: FBHP(t)=FBHPi×ea×t
The forecasted production characteristics component 110 may be configured to determine forecasted production characteristics of the well. Determining forecasted production characteristics may include ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, performing, quantifying, and/or otherwise determining the forecasted production characteristics. Determining forecasted production characteristics may include determining values of production characteristics at one or more moments in the future. Determining forecasted production characteristics may include determining separate values of production characteristics at separate moments in the future.
The forecasted production characteristics of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. The forecasted production characteristics of the well at multiple moments in the future may be determined based on the operation characteristics of the well and/or the production characteristics of the well. Time-series of the operation characteristics of the well and/or the production characteristics of the well may be used to determine time-series of the forecasted production characteristics of the well.
In some implementations, the forecasted production characteristics of the well may be determined based on the production characteristics of the well using a curve fitting analysis and/or other analyses. Curve fitting analysis may be used to extrapolate values of forecasted production characteristics of the well in the future based on values of historical production characteristics of the well in the past. For example, the forecasted production characteristics of the well may be determined based on the production characteristics (historical production characteristics) of the well using a decline curve analysis and/or other analyses. For example, the values of production characteristics may be modeled using hyperbolic decline with terminal exponential decline. Separate analysis may be performed for separate production characteristics of the wells (e.g., separate analysis for oil rate and water rate). Use of other analysis is contemplated.
The forecasted drawdown pressure component 112 may be configured to determine forecasted drawdown pressure of the well. Determining forecasted drawdown pressure may include ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, performing, quantifying, and/or otherwise determining the forecasted drawdown pressure. Determining forecasted drawdown pressure may include determining values of drawdown pressure at one or more moments in the future. Determining forecasted drawdown pressure may include determining separate values of drawdown pressure at separate moments in the future.
The forecasted drawdown pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. The forecasted drawdown pressure of the well at a particular moment in time may be determined based on the operation characteristics of the well at the particular moment in time and/or the production characteristics of the well at the particular moment in the past. Time-series of the operation characteristics and/or the production characteristics may be used to determine times series of the forecasted drawdown pressure of the well.
In some implementations, the forecasted drawdown pressure of the well may be determined based on the operation characteristics of the well and/or the production characteristics of the well by inputting the operation characteristics of the well and/or the production characteristics of the well into one or more machine learning models. The machine learning model(s) may be configured (e.g., trained) to output the forecasted drawdown pressure of the well. In response to the operation characteristics of the well and/or the production characteristics of the well for a particular moment in the future being input into the machine learning model(s), the machine learning model(s) may output the forecasted drawdown pressure of the well at the particular moment in the future. The machine learning model(s) outputting the forecasted drawdown pressure may include the machine learning model(s) outputting the value of forecasted drawdown pressure and/or information from which the value of the forecasted drawdown pressure may be determined. The machine learning model(s) may output separate values of forecasted drawdown pressure of the well for separate moments in the future.
In some implementations, the machine learning model may be trained based on shut-in simulation of the well and/or other information. For example, the machine learning model(s) may be trained as described with respect to step 428 of
The forecasted gas/oil ratio component 114 may be configured to determine forecasted gas/oil ratio of the well. Determining forecasted gas/oil ratio may include ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, performing, quantifying, and/or otherwise determining the forecasted gas/oil ratio. Determining forecasted gas/oil ratio may include determining values of gas/oil ratio at one or more moments in the future. Determining forecasted gas/oil ratio may include determining separate values of gas/oil ratio at separate moments in the future.
The forecasted gas/oil ratio of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. The forecasted gas/oil ratio of the well at a particular moment in time may be determined based on the operation characteristics of the well at the particular moment in time and/or the production characteristics of the well at the particular moment in the past. Time-series of the operation characteristics and/or the production characteristics may be used to determine times series of the forecasted gas/oil ratio of the well.
In some implementations, the forecasted gas/oil ratio of the well may be determined based on the operation characteristics of the well and/or the production characteristics of the well by inputting the operation characteristics of the well and/or the production characteristics of the well into one or more machine learning models. The machine learning model(s) may be configured (e.g., trained) to output the forecasted gas/oil ratio of the well. In response to the operation characteristics of the well and/or the production characteristics of the well for a particular moment in the future being input into the machine learning model(s), the machine learning model(s) may output the forecasted gas/oil ratio of the well at the particular moment in the future. The machine learning model(s) outputting the forecasted gas/oil ratio may include the machine learning model(s) outputting the value of forecasted gas/oil ratio and/or information from which the value of the forecasted gas/oil ratio may be determined. The machine learning model(s) may output separate values of forecasted gas/oil ratio of the well for separate moments in the future.
In some implementations, the machine learning model(s) may be trained based on fluid flow simulation of the well and/or other information. For example, the machine learning model(s) may be trained as described with respect to step 430 of
The production parameters component 116 may be configured to determine production parameters for the well. Determining production parameters may include ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, performing, quantifying, and/or otherwise determining the production parameters. Determining production parameters may include determining types and/or values of production parameters.
The production parameters for the well may be determined based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well, the forecasted drawdown pressure of the well, the forecasted gas/oil ratio of the well, and/or other information. The production parameters for the well at a particular moment in the future may be determined based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well (e.g., oil rate, water rate), the forecasted drawdown pressure of the well, and/or the forecasted gas/oil ratio of the well at the particular moment in the future. The production parameters for the well at multiple moments in the future may be determined based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well, the forecasted drawdown pressure of the well, and/or the forecasted gas/oil ratio of the well at the multiple movements in the future.
Production parameters for a well may refer to parameters (e.g., characteristics, factors, features, configurations, settings) that control, affect, reflect, and/or otherwise determine production from the well. Production parameters for a well may refer to parameters that control, affect, reflect, and/or otherwise determine how materials are produced using the well. In some implementations, the production parameters for the well may include shut-in bottom hole pressure. The shut-in bottom hole pressure of the well may serve as proxy for reservoir pressure in the unconventional reservoir. The production parameters for the well may further include water cut and productivity index. Other determined values may be used as production parameters for the well. For example, the production parameters for the well may include gas/oil ratio, flowing bottom hole pressure, and/or other determined values.
Production parameters for a well may be determined using one or more calculations. For example, the gas rate (QG) of a well may be determined based on the gas/oil ratio and the oil rate of the well. The gas rate of the well may be determined as the product of the gas/oil ratio and the oil rate of the well. The water cut of the well may be determined based on the water rate and the oil rate of the well. The water cut of the well may be determined as the ratio of the water rate to the sum of the oil rate and the water rate of the well. The shut-in bottom hole pressure (SBHP) of the well may be determined based on the flowing bottom hole pressure and the drawdown pressure of the well. The shut-in bottom hole pressure of the well may be determined as the sum of the flowing bottom hole pressure and the drawdown pressure of the well. The productivity index (PI) of the well may be determined based on the oil rate, the water rate, and the drawdown pressure of the well. The productivity index of the well may be determined as the ratio of the sum of the oil rate and the water rate to the drawdown pressure the of the well. Other calculation of production parameters is contemplated.
The operation component 118 may be configured to facilitate one or more operations of the well. The operation(s) of the well may be facilitated based on and/or other information. Facilitating an operation of a well may include carrying out, controlling, initiating, performing, scheduling, setting up, and/or otherwise facilitating the operation of the well. For example, facilitating an operation of a well may include determining, setting, and/or modifying one or more setpoints/parameters in how the well is operated. The operations of the well may be performed based on the production parameters for the well and/or other information. For example, the gas/oil ratio, the water cut, the flowing bottom hole pressure, the shut-in bottom hole pressure, and/or the productivity index may be used to set/modify the setpoints/parameters in how the well is operated. For instance, the setpoints/parameters may be set/modified to improve (e.g., increase efficiency of, optimize) well operations. The setpoints/parameters determined by the operation component 118 may be used to automatically control how the well is operated.
In some implementations, facilitation of the operation(s) of the well based on the production parameters for the well may include determination of artificial lift parameters for the well (e.g., gas lift injection rate, gas lift allocation, diluent injection, parameters of pumps, such as electrical submersible pumps, progressive cavity pumps, rod pumps, sucker rod pumps, jet pumps, hydraulic drive downhole pumps, multiphase pumps) based on the production parameters for the well and/or other information. The production parameters for the well may be used for production optimization (e.g., increasing/maximizing oil/gas production), drawdown management (e.g., increasing/maximizing estimated ultimate recovery), reservoir management (e.g., capacity management, controlling/adjusting load of different wells based on inflow performance relationships of the wells, reducing/eliminating loss production opportunity), and/or other operation of the well. The production parameters for the well may be used for production/well design (e.g., determining designs of artificial lift components, such as gas lift value, spacing, depth, pressure setting, pump size, motor type). In some implementations, facilitation of the operation(s) of the well based on the production parameters for the well may include determination of inflow performance relationship of the well based on the production parameters for the well and/or other information, and use of the inflow performance relationship of the well to facilitate operation(s) of the well.
In some implementations, the production parameters for the well and/or information determined from the production parameters for the well may be visually provided and/or presented to one or more users in controlling the operation of the well. For example, optimized setpoints/parameters for the wells may be communicated to an operator and/or presented on the electronic display 14. Other related information may be communicated to an operator and/or presented on the electronic display 14.
In some implementations, the production parameters for the well and/or information determined from the production parameters for the well may be audibly provided and/or played back to one or more users in controlling the operation of the well. For example, optimized setpoints/parameters for the wells may be audibly communicated to an operator and/or played on one or more speakers (e.g., loudspeaker, headphone). Other related information may be audibly communicated to an operator and/or played back on the speaker(s). In some implementations, information may be both visually and audibly provided to the user(s). For example, a user may be wearing a headset or holding a mobile device including a display and a speaker. Instructions for the well may be presented on the display and played out loud by the speaker.
Implementations of the disclosure may be made in hardware, firmware, software, or any suitable combination thereof. Aspects of the disclosure may be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a tangible computer-readable storage medium may include read-only memory, random access memory, magnetic disk storage media, optical storage media, flash memory devices, and others, and a machine-readable transmission media may include forms of propagated signals, such as carrier waves, infrared signals, digital signals, and others. Firmware, software, routines, or instructions may be described herein in terms of specific exemplary aspects and implementations of the disclosure, and performing certain actions.
In some implementations, some or all of the functionalities attributed herein to the system 10 may be provided by external resources not included in the system 10. External resources may include hosts/sources of information, computing, and/or processing and/or other providers of information, computing, and/or processing outside of the system 10.
Although the processor 11, the electronic storage 13, and the electronic display 14 are shown to be connected to the interface 12 in
Although the processor 11, the electronic storage 13, and the electronic display 14 are shown in
It should be appreciated that although computer program components are illustrated in
While computer program components are described herein as being implemented via processor 11 through machine-readable instructions 100, this is merely for ease of reference and is not meant to be limiting. In some implementations, one or more functions of computer program components described herein may be implemented via hardware (e.g., dedicated chip, field-programmable gate array) rather than software. One or more functions of computer program components described herein may be software-implemented, hardware-implemented, or software and hardware-implemented.
The description of the functionality provided by the different computer program components described herein is for illustrative purposes, and is not intended to be limiting, as any of computer program components may provide more or less functionality than is described. For example, one or more of computer program components may be eliminated, and some or all of its functionality may be provided by other computer program components. As another example, processor 11 may be configured to execute one or more additional computer program components that may perform some or all of the functionality attributed to one or more of computer program components described herein.
The electronic storage media of the electronic storage 13 may be provided integrally (i.e., substantially non-removable) with one or more components of the system 10 and/or as removable storage that is connectable to one or more components of the system 10 via, for example, a port (e.g., a USB port, a Firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage 13 may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EPROM, EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storage 13 may be a separate component within the system 10, or the electronic storage 13 may be provided integrally with one or more other components of the system 10 (e.g., the processor 11). Although the electronic storage 13 is shown in
In some implementations, methods 200, 250 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, a central processing unit, a graphics processing unit, a microcontroller, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of methods 200, 250 in response to instructions stored electronically on one or more electronic storage media. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of methods 200, 250.
Referring to
At operation 204, production information for the well may be obtained. The production information may define production characteristics of the well. In some implementations, operation 204 may be performed by a processor component the same as or similar to the production information component 104 (Shown in
At operation 206, historical flowing bottom hole pressure of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. In some implementations, operation 206 may be performed by a processor component the same as or similar to the historical flowing bottom hole pressure component 106 (Shown in
At operation 208, forecasted flowing bottom hole pressure of the well may be determined based on the historical flowing bottom hole pressure of the well and/or other information. In some implementations, operation 208 may be performed by a processor component the same as or similar to the forecasted flowing bottom hole pressure component 108 (Shown in
At operation 210, forecasted production characteristics of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. In some implementations, operation 210 may be performed by a processor component the same as or similar to the forecasted production characteristics component 110 (Shown in
Referring to
At operation 254, forecasted gas/oil ratio of the well may be determined based on the operation characteristics of the well, the production characteristics of the well, and/or other information. In some implementations, operation 254 may be performed by a processor component the same as or similar to the forecasted gas/oil ratio component 114 (Shown in
At operation 256, production parameters for the well may be determined based on the forecasted flowing bottom hole pressure of the well, the forecasted production characteristics of the well, the forecasted drawdown pressure of the well, the forecasted gas/oil ratio of the well, and/or other information. The production parameters for the well may include shut-in bottom hole pressure that serves as proxy for reservoir pressure in the unconventional reservoir. In some implementations, operation 256 may be performed by a processor component the same as or similar to the production parameters component 116 (Shown in
At operation 258, one or more operations of the well may be facilitated based on the production parameters for the well and/or other information. In some implementations, operation 258 may be performed by a processor component the same as or similar to the operation component 118 (Shown in
Although the system(s) and/or method(s) of this disclosure have been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the disclosure is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.
