Patent Application
20250163803
Oil and gas deposits may percolate up through subsurface pathways towards the Earth's surface over time by natural buoyancy. For example, various hydrocarbon deposits may remain trapped underground by geological barriers, which prevents oil and gas from reaching the surface. In this way, these hydrocarbon deposits may accumulate by displacing water in porous rock. Where the porous rock is also permeable, this hydrocarbon accumulation is commonly referred to as a conventional reservoir. As such, a well drilled into a conventional reservoir may normally cause oil and gas to flow through natural buoyancy, driven to a wellbore by reservoir pressures. In contrast, unconventional reservoirs may include hydrocarbon accumulations where oil and gas phases are tightly bound to the rock fabric by strong capillary forces. To access this accumulation, an unconventional reservoir may require specialized operations for evaluating and extracting the hydrocarbon deposits.
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.
In general, in one aspect, embodiments relate to a method that includes obtaining wellstream fluid data for one or more reservoir regions disposed in a well block that includes a first set of unconventional wells. The method further includes obtaining target data for the well block. The target data corresponds to a predetermined amount of well production and a predetermined amount of refined product. The method further includes determining, by a computer processor, predicted well production data based on the wellstream fluid data. The method further includes determining, by the computer processor, various production shrinkage values based on the wellstream fluid data. The method further includes determining, by the computer processor, predicted refined product data for the well block based on the production shrinkage values and the predicted well production data. The method further includes determining, by the computer processor, whether the predicted well production data and the predicted refined product data satisfy a predetermined criterion. The method further includes determining, by the computer processor, a second set of unconventional wells for the well block in response to the predicted refined product data failing to satisfy the predetermined criterion. The second set of unconventional wells is different from the first set of unconventional wells. The method further includes determining, by the computer processor, a well development plan based on the second set of unconventional wells. The method further includes performing one or more hydraulic stimulation operations at one or more wells based on the well development plan.
In general, in one aspect, embodiments relate to a system that includes a well control system coupled to a well. The system further includes an unconventional well manager that includes a computer processor. The unconventional well manager is coupled to the well control system. The unconventional well manager obtains wellstream fluid data for one or more reservoir regions disposed in a well block that includes a first set of unconventional wells. The unconventional well manager obtains target data for the well block. The target data corresponds to a predetermined amount of well production and a predetermined amount of refined product. The unconventional well manager determines predicted well production data for the well block based on the wellstream fluid data. The unconventional well manager determines various production shrinkage values based on the wellstream fluid data. The unconventional well manager determines predicted refined product data for the well block based on the production shrinkage values and the predicted well production data. The unconventional well manager determines whether the predicted well production data and the predicted refined product data satisfy a predetermined criterion. The unconventional well manager determines a second set of unconventional wells for the well block in response to the predicted refined product data failing to satisfy the predetermined criterion. The second set of unconventional wells is different from the first set of unconventional wells. The unconventional well manager determines a well development plan based on the second set of unconventional wells. The first well control system is configured to perform one or more hydraulic stimulation operations at the well based on the well development plan.
In some embodiments, selection of a set of unconventional wells is obtained for a well block. Predicted development cost data may be determined for the well block based on target data, wellstream fluid data, and surface equipment data. A determination may be made whether the predicted development cost data satisfies a predetermined criterion. In some embodiments, a set of unconventional wells is adjusted for a well block to produce a set of adjusted unconventional wells that satisfies a predetermined criterion. The second predetermined criterion may correspond to an economic value of well production and refined products that is produced by the well block exceeding a cost threshold for developing the second well block. In some embodiments, a refined product may include ethane, propane, butane, pentane, and condensate. Wellstream fluid data may describe one or more pressure-volume-temperature (PVT) properties of reservoir fluid based on one or more laboratory analyses. In some embodiments, a production shrinkage value describes a ratio of a predetermined amount of well production to a predetermined product yield.
In some embodiments, surface equipment data are obtained for a well block. Various production shrinkage values may be determined based on the surface equipment data. Predicted refined product data may be based on the surface equipment data. In some embodiments, a production constraint is obtained for a plant facility. The production constraint may correspond to a predetermined amount of input production that can be processed by the plant facility over a predetermined time interval; and adjusting an amount of well production over the predetermined time interval that is produced by a first unconventional well based on the production constraint.
In some embodiments, a downhole fluid sample is acquired, using a downhole sampling device, from a wellbore. The downhole sampling device may include a hydraulic fluid chamber, a sample chamber, a floating piston, a mechanical timer, a triggering system, a hanging head, and a closing mechanism. A multi-stage separator test may be performed on the downhole fluid sample to produce wellstream fluid data. In some embodiments, various production curves are obtained for a set of unconventional wells in a well block. Predicted well production data may be determined for the set of unconventional wells. A determination may be made whether the predicted well production data satisfies a predetermined criterion. The set of unconventional wells may be adjusted, in response to the predicted well production data failing to satisfy the predetermined criterion, to produce a different set of unconventional wells for the well block.
In some embodiments, various well blocks are determined for a set of unconventional wells within a geological region of interest. A respective well block among the well blocks may include a respective subset of wells of the set of unconventional wells. Respective target data for the respective well block may be determined accordingly. The respective subset of wells in the respective well block may be adjusted, based on the respective target data, to produce an adjusted subset of wells for the respective well block. In some embodiments, a first command is transmitted by a computer processor to a control system at an unconventional well among a set of unconventional wells based on predicted well production data. In some embodiments, a command is transmitted to a first control system at a plant facility based on predicted refined product data. In some embodiments, a selection of a set of unconventional wells is obtained for a well block using a graphical user interface that is presented on a display device. Predicted well production data for the well block may be determined for a first time interval and automatically using well data acquired over a well network coupling the set of unconventional wells. Predicted refined product data for the well block may be determined for the first time interval and automatically using plant data acquired over a plant network coupling various plant facilities. Predicted well production data and the predicted refined product data is presented in the graphical user interface.
In some embodiments, a plant facility is coupled to an unconventional well manager. The plant facility may transmit refined product data to the unconventional well manager. Various production shrinkage values may be determined based on the refined product data. In some embodiments, a downhole sampling device is coupled to a well. The downhole sampling device may acquire a downhole fluid sample from a wellbore coupled to the well. The downhole sampling device may include a hydraulic fluid chamber, a sample chamber, a floating piston, a mechanical timer, a triggering system, a hanging head, and a closing mechanism. Wellstream fluid data may be based on the downhole fluid sample. In some embodiments, a gathering system is coupled to a first well and a second well. an unconventional well manager may obtains surface equipment data regarding the gathering system. The predicted refined product data may be based on the surface equipment data.
In light of the structure and functions described above, embodiments of the invention may include respective means adapted to carry out various steps and functions defined above in accordance with one or more aspects and any one of the embodiments of one or more aspect described herein.
Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.
Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.
In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.
In general, embodiments of the disclosure include systems and methods for managing unconventional well developments by predicting well production data, refined product data, and/or development cost data for different well blocks. In some embodiments, for example, a reservoir region is divided into various well blocks or other areas in order to provide discrete well units for managing well activity in the respective units. Thus, well production and various refined products may be forecast for various unconventional reservoir assets using an unconventional well manager. Examples of unconventional reservoir assets may include shale formations, tight sand formations, basin-centered gas, coalbed methane (CBM), gas hydrates, tar sands, and oil-sand formations. The unconventional well manager may be an automated network controller on a well network that may obtain well data, refined product data, and user inputs from various entities in order to predict relevant data for managing well activity accordingly.
Furthermore, an unconventional well manager may collect information from various data sources connected to a well network, such as pressure-volume-temperature (PVT) data based on reservoir fluid samples, production curves for various production wells (e.g., based on acquired wellhead data or gathering system data), surface equipment data, and other well data. This collected information may subsequently be used to predict future well production that would be available for various plant facilities. Using subsurface assumptions include fluid composition, well type curves, and set production targets and surface assumptions (e.g., refining capacity at one or more plant facilities), different types of data may be predicted using statistical methods as well as one or more machine-learning techniques. Examples of surface assumptions may include number of available well areas, total available well locations in a particular well block, and production constraints (e.g., production handling capacity and scheduling constraints for well developments in a given year).
Additionally, well activity and production profiles for various well blocks may also be predicted and/or adjusted to matched desired target data (e.g., target amounts of sales gas and secondary products for a predetermined time interval). By adjusting well activity variables, such as the number of wells and the desired production curve over a particular period of time, an unconventional well manager may match calculated well activity to desired product targets. By forecasting production yields for a given set of well blocks or other well areas, an unconventional well manager may determine whether the predicted hydrocarbon outputs warrant the associated development costs to achieve the necessary well activity. For different economic price points for refined products and sales gas, for example, an unconventional well manager may optimize well development and actual well operations to meet one or more target economic goals.
Additionally, an unconventional well manager may also match actual economic expenditures with predicted revenue from the expected refined outputs. Examples of development costs may include costs for implementing stimulation operations, drilling operations, and surface equipment installations that connect production wells to plant facilities. As part of an economic evaluation workflow, predicted data may be used to generate reports with the final outputs as well as calculating cost estimates and projected revenues from different product streams. Thus, predicted data may be used to directly manage the development of one or more well blocks as well as manage ongoing well activity at each well block.
Furthermore, various embodiments may be used to determine well development plans for several unconventional oil and gas plays, such as shale formations. As such, an unconventional well manager may provide one or more automated workflows that provide flexibility for analyzing different reservoir conditions and surface accessibility conditions for potential unconventional wells. Based on different reservoir conditions, surface equipment infrastructures, and desired well locations, the unconventional well manager may predict well production and development costs for a particular reservoir region accordingly. Moreover, the unconventional well manager may automate documentation of well blocks in real-time, such as through identifying baseline imbedded system formulas and translating various business processes to new application platforms.
Turning to
In some embodiments, the well system (106) includes a wellbore (120), a well sub-surface system (122), a well surface system (124), and a well control system (126). The control system (126) may control various operations of the well system (106), such as well production operations, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations. In some embodiments, a well control system includes functionality for transmitting commands to another control system to implement a particular production operation or stimulation operation. For example, a well control system coupled to a reservoir simulator may transmit a network message over a machine-to-machine protocol to a control system based on predicted flow rate data. A command may be transmitted based on a user input or automatically based on changes in production conditions, e.g., after analyzing new reservoir data, electric-power data, and carbon emission data. In some embodiments, the control system (126) includes a computer system that is the same as or similar to that of computer system (602) described below in
The wellbore (120) may include a bored hole that extends from the surface (108) into a target zone of the hydrocarbon-bearing formation (104), such as the reservoir (102). An upper end of the wellbore (120), terminating at or near the surface (108), may be referred to as the “uphole” end of the wellbore (120), and a lower end of the wellbore, terminating in the hydrocarbon-bearing formation (104), may be referred to as the “downhole” end of the wellbore (120). The wellbore (120) may facilitate the circulation of drilling fluids during drilling operations, the flow of hydrocarbon production (“production”) (121) (e.g., oil and gas) from the reservoir (102) to the surface (108) during production operations, the injection of substances (e.g., water) into the hydrocarbon-bearing formation (104) or the reservoir (102) during injection operations, or the communication of monitoring devices (e.g., logging tools) into the hydrocarbon-bearing formation (104) or the reservoir (102) during monitoring operations (e.g., during in situ logging operations).
In some embodiments, during operation of the well system (106), the control system (126) collects and records wellhead data (140) for the well system (106). The wellhead data (140) may include, for example, a record of measurements of wellhead pressure (P) (e.g., including flowing wellhead pressure), wellhead temperature (T) (e.g., including flowing wellhead temperature), wellhead production rate (Q) over some or all of the life of the well system (106), and water cut data. In some embodiments, the measurements are recorded in real-time, and are available for review or use within seconds, minutes or hours of the condition being sensed (e.g., the measurements are available within 1 hour of the condition being sensed). In such an embodiment, the wellhead data (140) may be referred to as “real-time” wellhead data (140). Real-time wellhead data (140) may enable an operator of the well system (106) to assess a relatively current state of the well system (106) and make real-time decisions regarding development of the well system (106) and the reservoir (102), such as on-demand adjustments in regulation of production flow from the well.
In some embodiments, the well surface system (124) includes a wellhead (130). The wellhead (130) may include a rigid structure installed at the “uphole” end of the wellbore (120), at or near where the wellbore (120) terminates at the Earth's surface (108). The wellhead (130) may include structures for supporting (or “hanging”) casing and production tubing extending into the wellbore (120). Production (121) may flow through the wellhead (130), after exiting the wellbore (120) and the well sub-surface system (122), including, for example, the casing and the production tubing. In some embodiments, the well surface system (124) includes flow regulating devices that are operable to control the flow of substances into and out of the wellbore (120). For example, the well surface system (124) may include one or more production valves (132) that are operable to control the flow of production (121). For example, a production valve (132) may be fully opened to enable unrestricted flow of production (121) from the wellbore (120), the production valve (132) may be partially opened to partially restrict (or “throttle”) the flow of production (121) from the wellbore (120), and production valve (132) may be fully closed to fully restrict (or “block”) the flow of production (121) from the wellbore (120), and through the well surface system (124).
Keeping with
In some embodiments, the surface sensing system (134) includes a surface pressure sensor (136) operable to sense the pressure of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface pressure sensor (136) may include, for example, a wellhead pressure sensor that senses a pressure of production (121) flowing through or otherwise located in the wellhead (130). In some embodiments, the surface sensing system (134) includes a surface temperature sensor (138) operable to sense the temperature of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface temperature sensor (138) may include, for example, a wellhead temperature sensor that senses a temperature of production (121) flowing through or otherwise located in the wellhead (130), referred to as “wellhead temperature” (T). In some embodiments, the surface sensing system (134) includes a flow rate sensor (139) operable to sense the flow rate of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The flow rate sensor (139) may include hardware that senses a flow rate of production (121) (Q) passing through the wellhead (130).
In some embodiments, the well system (106) includes a reservoir simulator (160). For example, the reservoir simulator (160) may include hardware and/or software with functionality for generating one or more reservoir models regarding the hydrocarbon-bearing formation (104) and/or performing one or more reservoir simulations. For example, the reservoir simulator (160) may store well logs and data regarding core samples for performing simulations. A reservoir simulator may further analyze well log data, wellstream fluid data, core sample data, seismic data, surface well data, downhole data, and/or other types of geophysical data to generate and/or update the one or more reservoir models. While the reservoir simulator (160) is shown at a well site, embodiments are contemplated where reservoir simulators are located away from well sites. In some embodiments, the reservoir simulator (160) may include a computer system that is similar to the computer system (602) described below with regard to
In some embodiments, downhole pressure sensors include absolute pressure transmitters, differential-pressure transmitters, and/or multivariable transmitters. Absolute pressure transmitters may include sensors that measure pressure with respect to a full vacuum, while differential-pressure transmitters may include sensors that are used in flow applications. Multivariable transmitters may measure pressure in addition to other variables, such as temperature. For example, a multivariable transmitter may be a gauge sensor that measures both pressure and temperature at a single point, such as a single quartz crystal. Multivariable transmitters may be transmit-only devices in a well providing pressure and temperature (PT) measurements at fixed time intervals, e.g., using one or more electric lines and one or more hydraulic lines. Likewise, multivariable transmitters may transmit pressure and temperature data to a well surface using a high-speed digital telemetry link. Similar to downhole pressure sensors, downhole temperature sensors may include downhole temperature gauges, temperature transmitters, and/or multivariable transmitters. In some embodiments, permanent downhole gauges (PDGs) are used that are permanently installed in a well and used to detect pressure data and/or temperature data.
In some embodiments, a flow rate sensor is a multiphase flow meter. For example, a multiphase flow meter may include hardware and/or software for determining individual flow rates of different components within a three-phase flow. More specifically, a multiphase flow meter may determine a mass flow rate of a gas component and a mass flow rate of a liquid component (e.g., a component of the three-phase flow that includes oil and water) of the three-phase flow. As such, a multiphase flow meter may be used to determine an amount of oil or a portion of oil within a multiphase flow that travels through a wellhead during a given period of time. A multiphase flow meter may also include hardware that uses various types of sensors based on different sensing technologies (e.g., nuclear magnetic resonance, electromagnetic sensors, acoustic sensors, etc.) and interpretation models. For example, a multiphase flow meter may use a sensor response of magnetic resonance information to determine the number of hydrogen atoms in a particular fluid flow. Since oil, gas, and water each contain hydrogen atoms, properties of a multiphase flow may be measured using magnetic resonance. The hydrogen atoms in a magnetized fluid may respond to radio frequency pulses and emit echoes that are subsequently recorded and analyzed by the multiphase flow meter. Thus, multiphase flow rate measurements may be used for production monitoring, well control, and/or reservoir optimization.
Moreover, a multiphase flow metering system may include a multiphase flow meter and a host device. In response to determining flow rate data regarding a multiphase flow, a multiphase flow meter may transmit flow rate data to a host device, such as a well control system or another type of computer system, over a network. The multiphase flow meter may be coupled to one or more flow tubes in order to determine the flow rate data, such as individual flow rates and/or oil, gas, and/or water fractions of a corresponding multiphase flow. A flow tube may be a fluid conduit, such as pipe, that may provide a fluid sampling for analysis by the multiphase flow meter. Examples of flow tubes may include a bent flow tube, a straight flow tube, or another type of flow tube. Furthermore, a flow model may be stored within a multiphase flow meter as a portion of a database and/or as one or more flow regime maps that are associated with various sensor values. By analyzing sensor data in connection with one or more flow models, a flow meter may determine flow rate data that corresponds to acquired sensor data. Flow rate data may include corresponding fractional data (e.g., gas fraction of a multiphase flow) and/or velocity data (e.g., an individual flow rate of oil or water in the multiphase flow).
Furthermore, the multiphase flow meter may include a flow meter controller that controls sensing operations and/or the flow analysis operations. In some embodiments, a flow controller uses one or more flow models to determine flow rate data regarding a particular flow. Phase distribution information may describe the respective fractions of one or more phases (e.g., gas phase, oil phase, water phase), in a particular flow. Flow regime information may refer to a specific manner that two or three phases flow through a flow tube. For example, a flow regime may be expressed using various superficial velocities. One example of a flow regime may be a “bubble regime,” in which gas is entrained as bubbles within a liquid. Another example of a flow regime is a “slug regime” that may correspond to a series of liquid “slugs” or “plugs” separated by relatively large gas pockets. Accordingly, a flow model may describe changes in a multiphase flow between transitions from high-liquid compositions to high-gas compositions and vice versa. Other flow regimes may include an annular flow regime, a dispersed flow regime, and a froth flow regime.
In some embodiments, one or more production logging tools (PLT) are used to determine production data at one or more depth intervals in a production well. For example, PLT data at a particular depth may include wellbore temperature measurements, pressure measurements, fluid density measurements, flow velocity measurements, and holdup data (e.g., volume fraction of a pipe occupied by fluid). While measurements of pressure, temperature and flow rate can be obtained at the surface, surface measurements may not necessarily reflect what is happening in the reservoir. As such, PLT data may be acquired downhole using various logging tools. More specifically, fluid velocity data may be acquired using a spinner flowmeter. In particular, a spinner flowmeter may include a rotating blade that turns when fluid moves past the device (e.g., the rotational speed of the blade in revolutions per second (RPS) may be proportional to the fluid velocity). Moreover, PLT data may be acquired using a production logging toolstring. This toolstring may include a fullbore spinner, various fluid holdup and bubble count probes, a pipe diameter caliper tool, a bearing sensor, a pressure sensor, a temperature sensor, a gamma ray tool, a casing collar locator, one or more batteries, and/or a data recorder. Other production logging tools include markers/tracers such as oxygen activation logs or radioactive iodine tracer logs as well as anemometers. An anemometer may be an instrument that measures the speed or velocity of gases in a contained flow. As such, PLT operations may include temperature logging, radioactive tracer logging, noise logging, focused gamma ray density logging, unfocused gamma ray density logging, fluid capacitance logging, fluid identification logging in high angle wells, and flowmeter logging at different depth intervals.
Keeping with production logging tools, various types of logging tools may be used to provide information during production operations and afterwards. For example, production logging may determine an axial flow rate based on axial velocity data and an internal diameter of a pipe component. Likewise, production logging may be used to track movement of fluid either inside or immediately outside the casing of a wellbore. Examples of production logs include temperature surveys, mechanical flowmeter surveys, and borehole fluid-density surveys, and fluid-capacitance surveys. Moreover, PLT data may be used to determine whether a production problem exists, such as excessive water or gas production. In particular, PLT data may be used to determine whether a production problem is the result of a completion problem or a reservoir problem. In some embodiments, production logging is used to determine the location of casing damage or collars. Likewise, PLT data may also determine water holdup in a wellbore or a well's gas volume fraction. Thus, PLT data may provide detailed, multiphase evaluation of fluid velocity and phase identification in vertical, deviated, and horizontal wells. More specifically, PLT data may identify fluid entry, gas leaks, injection zones, and cement tops through various well or reservoir analyses.
In some embodiments, the well system (106) includes a water cut sensor. For example, a water cut sensor may be hardware and/or software with functionality for determining the water content in oil, also referred to as “water cut.” Measurements from a water cut sensor may be referred to as water cut data and may describe the ratio of water produced from the wellbore (120) compared to the total volume of liquids produced from the wellbore (120). Water cut sensors may implement various water cut measuring techniques, such as those based on capacitance measurements, Coriolis effect, infrared (IR) spectroscopy, gamma ray spectroscopy, and microwave technology. Water cut data may be obtained during production operations to determine various fluid rates found in production from the well system (106).
With regard to microwave based water cut sensors, certain microwave-based water cut sensors may rely on measuring a phase difference between transmitted and received microwave signals. As such, the phase difference may have a direct link with the effective permittivity of the oil and water mixture from the wellbore (120). In some embodiments, microwave-based water cut sensors employ transmit (Tx) antennas and receive (Rx) antennas disposed inside of well system pipe, such that the antennas are at least partially immersed in the fluid mixture as the fluid flows through the pipe.
In some embodiments, the well system (106) includes a water cut sensing system that includes a water cut (WC) sensor, a cylindrical pipe, and/or a measurement processing system. The WC sensor may be disposed on (or otherwise integrated within) the cylindrical pipe. As such, the WC sensor may include a signal conductor (SC) (e.g., a first conductive plane), such as a T-resonator, disposed at a first/upper/top surface of the cylindrical pipe, and a ground conductor (GC) (e.g., a second conductive plane) disposed at a second/lower/bottom surface of the cylindrical pipe that is opposite the first/upper/top surface of the pipe. In such a configuration, the WC sensing system may be employed to sense a water cut of fluid obtained from the wellbore (120) (e.g., a water and oil mixture, or other substrate). In some embodiments, a WC sensor includes multiple waveguides that are attached to a production pipe, where a network analyzer may be connected to the waveguides. The network analyzer may be communicatively coupled with the well control system (126) to determine water cut data.
In some embodiments, production wells and/or injection wells are involved in one or more stimulation operations. For example, one type of stimulation operation is a water-alternating-gas (WAG) operation. A WAG operation may be a cyclic process of injecting water followed by gas. Using a WAG injection, macroscopic or microscopic sweep efficiency may be improved for a reservoir, e.g., by maintaining nearly initial high pressure, slow down any gas breakthroughs, and reduce oil viscosity. Likewise, WAG injections may also decrease residual oil saturation resulting from three phase flows and effects associated with relative permeability hysteresis. Thus, some stimulation operations may produce gas flooding, which is a type of enhanced oil recovery (EOR) method for increasing recovery of light to moderate oil reservoirs. In some stimulation operations, water may be injected during the initial phase of the operation and followed by a gas (e.g., carbon dioxide) because water may have a higher mobility ratio than the injected gas, thereby preventing breakthroughs in the reservoir. Injected gas may be a mixture of hydrocarbon gas or nonhydrocarbon gases. With hydrocarbon gases, the gas mixture may include methane, ethane, and propane for achieving a miscible or immiscible gas-oil system in the reservoir. With nonhydrocarbon gases, the gas mixture may include carbon dioxide (CO2), nitrogen (N2), and some exotic gases that displace fluid in the reservoir. Likewise, gas may also be injected directly into a reservoir, e.g., into the gas cap, to compensate for the reservoir's pressure decline.
Furthermore, a stimulation injection during a stimulation operation may correspond to various injection parameters, such as bank size, cycle time, and a predetermined water-gas ratio (also called a “WAG ratio”). Bank size may refer to a size of sequential banks of fluids (e.g., oil, CO2 and water) formed in the reservoir rock in response to a stimulation operation that migrate from the injection to the production wells. For illustration, a WAG ratio of 1:1 may result in a high oil production for one or more production wells, such as production wells coupled to a miscible reservoir. Based on some reservoir parameters such as oil composition, gas flooding can be carried out in miscible or immiscible conditions. Moreover, different types of stimulation operations may use different stimulation parameters. Examples of different stimulation operations may include: (1) continuous gas injections; (2) WAG injections; (3) simultaneous water-alternating-gas (SWAG) injections; and (4) tapered WAG injections. Different strategies have been developed by the petroleum industry to cope with these conditions.
Keeping with
In some embodiments, a logging system may include one or more logging tools for use in generating well logs of the formation. For example, a logging tool may be lowered into a wellbore to acquire measurements as the tool traverses a depth interval (e.g., a targeted reservoir section) of the wellbore. The plot of the logging measurements versus depth may be referred to as a “log” or “well log”. Well logs may provide depth measurements of the well that describe such reservoir characteristics as formation porosity, formation permeability, resistivity, water saturation, and the like. The resulting logging measurements may be stored and/or processed, for example, by the control system, to generate corresponding well logs for a well. A well log may include, for example, a plot of a logging response time versus true vertical depth (TVD) across the depth interval of the wellbore.
Turning to examples of logging techniques, multiple types of logging techniques are available for determining various reservoir characteristics (e.g., wireline logging, logging-while-drilling (LWD), and measurement-while-drilling (MWD)). In some embodiments, gamma ray logging is used to measure naturally occurring gamma radiation to characterize rock or sediment regions within a wellbore. In particular, different types of rock may emit different amounts and different spectra of natural gamma radiation. For example, gamma ray logs may distinguish between shales and sandstones/carbonate rocks because radioactive potassium may be common to shales. Likewise, the cation exchange capacity of clay within shales may also result in higher absorption of uranium and thorium further increasing the amount of gamma radiation produced by shales.
Turning to nuclear magnetic resonance (NMR) logging, an NMR logging tool may measure the induced magnetic moment of hydrogen nuclei (i.e., protons) contained within the fluid-filled pore space of porous media (e.g., reservoir rocks). Thus, NMR logs may measure the magnetic response of fluids present in the pore spaces of the reservoir rocks. In so doing, NMR logs may measure both porosity and permeability, as well as the types of fluids present in the pore spaces. Thus, NMR logging may be a subcategory of electromagnetic logging that responds to the presence of hydrogen protons rather than a rock matrix. Because hydrogen protons may occur primarily in pore fluids, NMR logging may directly or indirectly measure the volume, composition, viscosity, and distribution of pore fluids.
Turning to coring, reservoir characteristics may be determined using core sample data acquired from a well site. For example, certain reservoir characteristics can be determined via coring (e.g., physical extraction of rock specimens) to produce core specimens and/or logging operations (e.g., wireline logging, logging-while-drilling (LWD) and measurement-while-drilling (MWD)). Coring operations may include physically extracting a rock specimen from a region of interest within the wellbore for detailed laboratory analysis. For example, when drilling an oil or gas well, a coring bit may cut core plugs (or “cores” or “core specimens”) from the formation and bring the core plugs to the surface, and these core specimens may be analyzed at the surface (e.g., in a lab) to determine various characteristics of the formation at the location where the specimen was obtained.
Turning to various coring technique examples, conventional coring may include collecting a cylindrical specimen of rock from the wellbore using a core bit, a core barrel, and a core catcher. The core bit may have a hole in its center that allows the core bit to drill around a central cylinder of rock. Subsequently, the resulting core specimen may be acquired by the core bit and disposed inside the core barrel. More specifically, the core barrel may include a special storage chamber within a coring tool for holding the core specimen. Furthermore, the core catcher may provide a grip to the bottom of a core and, as tension is applied to the drill string, the rock under the core breaks away from the undrilled formation below a coring tool. Thus, the core catcher may retain the core specimen to avoid the core specimen falling through the bottom of the drill string. In some embodiments, a micro computed tomography (micro-CT) scan is performed on a core sample. Several types of micro-CT scanning may be used, such as a desktop micro-CT scanner that uses an X-ray generation tube, and a synchrotron X-ray micro-tomography. In particular, a micro-CT scanner may use various X-rays to penetrate from different viewpoints in a core sample to produce an attenuated projection profile that is used for later reconstruction using a filtered back projection algorithm.
Furthermore, cutting samples may be acquired and analyzed from one or more drilling operations to determine various geological properties of one or more formations. In particular, cuttings may be initially cleaned in liquid detergent to remove drilling additives and before being dried on a ‘hotplate’. Dried cutting samples may be passed through one or more sieves to remove fragments of various sizes. Likewise, a magnet may be placed over a sieved cutting sample to remove any metallic fragments acquired during a drilling operation. After selecting various desired samples from the sieving and other preparation processes, selected samples may be ground into a fine powder for analysis using X-ray fluorescence (XRF) spectrometry processing and/or and inductively coupled plasma (ICP) spectrometry processing.
Turning to downhole sampling, some embodiments acquire various downhole fluid samples of fluids using one or more downhole sampling devices. Downhole fluid sampling may also be referred to as bottomhole sampling. In particular, downhole fluid samples may include samples of reservoir fluids as well as active production streams above ambient pressure. A downhole sampling operation of a production stream may involve running a sampling tool (e.g., a downhole sampling device) into a well using wireline technology to acquire a fluid sample under the increased pressure of the fluid column. As such, careful well conditioning may be necessary to ensure that the downhole fluid sample is in a monophasic condition. For example, a downhole sampling device (e.g., downhole sampling devices X (214)) may include a timer with a mechanical clock or be connected to the surface by an electric line that conveys an electric triggering signal for acquiring a sample at a predetermined depth or location. In some embodiments, a downhole sampling device may be lowered into the well until the tool is a short distance above the upper limit of a perforated interval to collect a fluid sample that is representative of various production intervals. As an alternative to wireline technology, downhole sampling devices may also be implemented using drillstem and tubing-conveyed installations. In some embodiments, a formation-test tool is a single-phase reservoir sampler (SRS) device that maintains a formation sample in a single-phase condition above reservoir pressure as the SRS device is retrieved from a wellbore. An SRS device may have its own clock for determining when and at what depth to acquire a downhole sample. In some embodiments, a downhole sampling device includes a hydraulic fluid chamber, a sample chamber, a floating piston, a mechanical timer, a triggering system, a hanging head, and a closing mechanism.
In some embodiments, reservoir-fluid samples are acquired using one or more formation-testing tools. For example, a formation-testing tool may be inserted into an openhole well containing drilling mud or completion fluid. Once the formation-testing tool has been run to a predetermined depth, the formation-testing tool may force a probe against the formation. The probe may provide a seal against the borehole wall such that only formation fluid can flow into the formation-testing tool. Likewise, a formation-testing tool may be equipped with various devices designed to collect samples of reservoir fluid in a series of sample chambers. As such, a formation-testing tool may collect reservoir fluid without performing a drill stem test (DST) and flowing fluid to the surface as well as acquiring sample fluids from a number of discrete depths (e.g., for identifying a reservoir fluid gradient).
Keeping with reservoir and downhole sampling, some embodiments use surface-separator sampling where reservoir-fluid samples are recombined in a laboratory. However, recombination procedures may result in gas-oil ratio (GOR) errors and measurement imprecision. As such, downhole sampling may avoid such inaccuracies by acquiring reservoir fluid in a monophasic condition when sampled. Thus, some embodiments use laboratory analyses that include both surface samples at the wellhead and downhole fluid samples to obtain a representative reservoir fluid. For an SRS device, an unaltered sample may be retrieved at the surface in a single-phase state, thus requiring no recombination.
In some embodiments, downhole samples are used to determine pressure-volume-temperature (PVT) properties of one or more regions in an unconventional reservoir. In particular, a PVT laboratory test on a downhole fluid sample may use multiple stages. For example, separator test experiments may be carried out for both oil and gas condensate mixtures. A sample of reservoir fluid may be placed in a laboratory cell and brought to reservoir temperature and bubble-point pressure. Afterwards, fluid may be expelled from the laboratory cell through a number of stages of separation. Usually, two or three stages of separation are used, with the last stage at atmospheric pressure and near-ambient temperature.
Keeping with PVT data, PVT properties may be used for hydrocarbon reserve estimations, reservoir modeling, production and pressure analysis, and for predicting well production performance. Thus, PVT properties may be identified by relating specific properties of unconventional reservoir fluids with various reservoir measurements, such as saturation pressure and oil formation volume factor may be correlated with reservoir temperature, stock tank oil gravity, specific gas gravity, and/or solution gas-oil-ratios. More specifically, PVT may be determined using various PVT correlation methods, such as non-parametric correlation methods that provide a multivariate optimization without using a specific model. Examples of PVT correlation methods may include exponential-polynomial functions and rational polynomial functions. In addition to PVT correlation methods, PVT properties may be further determined using equation-of-state (EOS). Equations-of-state may be computationally complex, thereby requiring detailed compositions of reservoir fluids. An example of EOS is a mathematical function that relates pressure, molar volume, temperature, and composition for modelling a fluid system (e.g., a reservoir region).
Turning to
In some embodiments, one or more unconventional wells are coupled to a gathering system (e.g., gathering system X (225)). A gathering system (also referred to as a collecting system or gathering facility) may include various hardware arrangements that connect flowlines from several unconventional wells into a single gathering line. For example, a gathering system may include flowline networks, headers, pumping facilities, separators, emulsion treaters, compressors, dehydrators, tanks, valves, regulators, and/or associated equipment. In particular, a remote header (e.g., remote headers X (216)) may have production valves and testing valves to control a mixed stream for a flowline of a respective unconventional well. Thus, a gathering system may direct various hydrocarbon fluids to a processing or testing facility, such as a gas processing plant or a refinery. In some embodiments, a gathering system manages individual fluid ratios (e.g., a particular gas-to-water ratio or condensate-to-gas ratio) as well as supply rates of oil, gas, and water. For example, a gathering system may assign a particular production value or ratio value to a particular gas well by opening and closing selected valves among the remote headers and using individual metering equipment or separators. Furthermore, a gathering system may be a radial system or a trunk line system. A radial system may bring various flowlines to a single central header. In contrast, a trunk-line system may use several remote headers to collect oil and gas from fields that cover a large geographic area. Once collected, the gathering system may transport and control the flow of oil or gas to a storage facility, a refinery, a gas processing plant, or a shipping point.
Keeping with
Keeping with plant facilities, a gas processing plant may include water processing equipment that includes hardware and/or software for extracting, treating, and/or disposing of water associated with gas processing. More specifically, a gas plant may extract produced water during the separation of oil or gas from a mixed fluid stream (e.g., mixed fluid stream (285)) acquired from a gas well. This produced water may be a kind of brackish and saline water brought to the surface from underground formations. In particular, oil and gas reservoirs may have water in addition to hydrocarbons in various zones underneath the hydrocarbons, and even in the same zone as the oil and gas. However, most produced water is of very poor quality and may include high levels of natural salts and minerals that have dissociated from geological formations in the target reservoir. Likewise, produced water may also acquire dissolved constituents from fracturing fluids (e.g., substances added to the fracturing fluid to help prevent pipe corrosion, minimize friction, and aid the fracking process). However, through various water treatments, produced water may be reused in one or more gas wells, e.g., through waterflooding where produced water is injected into the reservoirs. By injecting produced water into an injection well, the injected water may force oil and gas to one or more production wells.
In some embodiments, a plant facility may include one or more storage facilities (e.g., storage facility B (271)), one or more control systems (e.g., control systems B (272)). For example, different forms of gas and other petroleum products may be stored in various storage facilities, such as surface containers. With respect to control systems, a control system may include hardware and/or software that monitors and/or operates equipment, such as machinery for operating a gas well, a gas processing plant, a refinery, or other chemical manufacturing plant. Examples of control systems may include one or more of the following: an emergency shut down (ESD) system, a safety control system, a video management system (VMS), process analyzers, other industrial systems, etc. In particular, a control system may include a programmable logic controller that may control valve states, fluid levels, pipe pressures, warning alarms, pressure releases and/or various hardware components throughout a facility. Thus, a programmable logic controller may be a ruggedized computer system with functionality to withstand vibrations, extreme temperatures, wet conditions, and/or dusty conditions, such as those around a refinery or drilling rig.
With respect to control systems, control systems may include a programmable logic controller (PLC), a distributed control system (DCS), a supervisory control and data acquisition (SCADA), and/or a remote terminal unit (RTU). For example, a programmable logic controller may control valve states, fluid levels, pipe pressures, warning alarms, and/or pressure releases throughout a well facility or power-generation facility. In particular, a programmable logic controller may be a ruggedized computer system with functionality to withstand vibrations, extreme temperatures, wet conditions, and/or dusty conditions, for example, around a production well. A distributed control system may be a computer system for managing various processes at various facilities using multiple control loops. As such, a distributed control system may include various autonomous controllers (such as remote terminal units) positioned at different locations throughout the facility to manage operations and monitor processes. Likewise, a distributed control system may include no single centralized computer for managing control loops and other operations. On the other hand, a SCADA system may include a control system that includes functionality for enabling monitoring and issuing of process commands through local control at a facility as well as remote control outside the facility. With respect to an RTU, an RTU may include hardware and/or software, such as a microprocessor, that connects sensors and/or actuators using network connections to perform various processes in the automation system. Likewise, a control system may be coupled to one or more well devices.
Keeping with control systems, a control system may be coupled to plant facility equipment. Facility equipment may include various machinery such as one or more hardware components that may be monitored using one or more sensors. Examples of hardware components coupled to a control system may include crude oil preheaters, heat exchangers, pumps, valves, compressors, loading racks, and storage tanks among various other types of hardware components. Hardware components may also include various network elements or control elements for implementing control systems, such as switches, routers, hubs, PLCs, remote terminal units, user equipment, or any other technical components for performing specialized processes. Examples of sensors may include pressure sensors, torque sensors, rotary switches, weight sensors, position sensors, microswitches, hydrophones, accelerometers, etc. An unconventional well manager, user devices, and network elements may be computer systems similar to the computer system (602) described in
In some embodiments, a well network includes an unconventional well manager (e.g., unconventional well manager X (250)) that includes hardware and/or software for collecting data in real-time from various unconventional wells, gas processing plants, refineries, user devices, and other systems in the well network. More specifically, an unconventional well manager may include functionality for obtaining data throughout the well network, such as unconventional well data (e.g., unconventional well data X (290)) and/or refined product data (e.g., refined product data N (276)). For example, unconventional well data may include testing data of potential flow rates, flowing wellhead pressure (FWHP), water-gas ratio (WGR) data, condensate data such as condensate-gas ratio (CGR) data, productivity index (PI) data, water sampling data (e.g., levels of Chloride and Strontium concentrations), and congestion data regarding congestion cycles of gas wells.
In some embodiments, an unconventional well manager includes functionality for determining and/or implementing one or more development plans for one or more well blocks in real-time based on unconventional well data (e.g., production curve data B (252), wellstream fluid data E (255), unconventional well data X (290)), refined product data (e.g., predicted refined product data D (254), refined product data N (276), refined product data B (273)), target data (e.g., target data A (251)), predicted well production data (e.g., predicted well production data C (253)), production shrinkage values (e.g., production shrinkage data G (259)), predicted development cost data (e.g., predicted development cost data E (258)), production constraints (e.g., production constraints G (257)), and/or surface equipment data (e.g., surface equipment data F (256)). In particular, a development plan may allocate different well production targets to different unconventional wells, different reservoirs, and/or different well blocks. For a desired amount of secondary product (e.g., based on refining capacity of various plant facilities), various unconventional wells may be developed and operated to achieve the desired amount of secondary product. Moreover, the unconventional well manager may automatically prioritize well production, refined production, stimulation operations, and well development instantaneously by incorporating various time-dependent production demand scenarios into a development plan. In some embodiments, an unconventional well manager includes functionality for generating periodic development plans, such as yearly development plans.
In some embodiments, a user device (e.g., user device M (230)) may communicate with an unconventional well manager to adjust dynamically a particular production scenario based on one or more user selections (e.g., user selections N (231)). The user device may be a personal computer, a handheld computer device such as a smartphone or personal digital assistant, or a human machine interface (HMI). For example, a user may interact with a user interface (e.g., user interface O (232)) to change a time interval of a particular development plan. Through user selections or automation, the unconventional well manager may maintain well block performance, manage development in various well areas, and raise future supply awareness by presenting well clusters and associated information in a graphical user interface. As such, an unconventional well manager may provide agility and flexibility in determining and modifying well development plans.
In some embodiments, a well development plan is generated by an unconventional well manager upon obtaining a request from a user device and using various predetermined criteria, such as target data (e.g., a request for a well development plan (233) or a request to predict refined production data or well production data for a well block). Examples of predetermined criteria include desired well production levels, desired refined product levels, desired well development costs, and other specific well activity attributes (e.g., a maximum number of wells for one or more well blocks). Likewise, a predetermined criterion may correspond to predicted data for a particular period of time and/or a specific well area. The request may be a network message transmitted between a user device and an unconventional well manager that identifies various unconventional wells, gas processing plants, refineries, gathering systems, a predetermined time frame, and other parameters for a requested development plan. In some embodiments, the unconventional well manager includes functionality for transmitting commands (e.g., command Y (295)) to one or more control systems to implement a particular unconventional well development plan. For example, an unconventional well manager X (250) may transmit a network message over a machine-to-machine protocol to a well system in gas well or one or more of control systems in a refinery. A command may be transmitted periodically, based on a user input, or automatically based on changes in unconventional well data or refined product data.
Turning to
Keeping with
Keeping with hydraulic fracturing, a hydraulic fracturing operation may include well completion assembly with one or more inflatable packers as well as a work string or casing string (306) that extends within a wellbore. A casing string may include steel casing or pipe that may be divided into surface casing, intermediate casing, and/or production casing. Packers may include inflatable packers that seal an annulus defined between well completion equipment and an inner wall of the wellbore in order to divide a formation into multiple wellbore intervals. These wellbore intervals may be separately or simultaneously stimulated during a hydraulic stimulation operation using a stimulation control system. Thus, in a hydraulic fracturing operation, a hydraulic fracturing fluid may be pumped through the casing string (306) and into a targeted formation using various perforations (i.e., open holes) in the casing string (306).
By injecting the hydraulic fracturing fluid at pressures high enough to cause the rock within the targeted formation to fracture, the hydraulic fracturing operation may “break down” the formation. As high-pressure fluid injection continues, a fracture may continue to propagate into a fracture network. This high pressure for injecting the hydraulic fracturing fluid may be referred to as the “propagation pressure” or “extension pressure.” As an induced fracture continues to grow, a proppant, such as sand, may be added to the fracturing fluid. Once a desired fracture network is formed, the fluid flow may be reversed, and the liquid portion of the fracturing fluid may be removed. The proppant is intentionally left behind to prevent the fractures from closing onto themselves due to the weight and stresses within the formation. Accordingly, the proppant may “prop” or support the induced fractures to remain open, by remaining sufficiently permeable for hydrocarbon fluids to flow through the induced fracture. Thus, a proppant may form a packed bed of particles with interstitial void space connectivity within a formation. Accordingly, a higher permeability fracture may result from the hydraulic fracturing operation.
In some embodiments, for example, a hydraulic fracturing fluid with an activator is injected into the formation (302), where the fluid migrates within the large fractures (310). Upon a reaction caused by the activator, the injection fluid may produce one or more gases and heat, thereby causing the microfractures (312) to be created within the formation (302). Thus, a stimulation treatment may provide pathways for the hydrocarbon deposits trapped within the formation (302) to migrate and be recovered by a production well. In other words, hydraulic stimulation operations may be applied to formations that easily fracture to produce more microfractures with little plastic deformation under compression.
Furthermore, fracture monitoring may be important to understanding and optimizing hydraulic fracturing treatments. For example, a hydraulic stimulation manager may perform diagnostics that determine various stimulation effects such as fracture geometry, proppant placement in one or more fractures, and/or fracture conductivity. This fracture monitoring may be performed using a distributed acoustic sensing (DAS) system implemented within a wellbore. In some embodiments, a DAS system includes various fiber-optic sensors (e.g., distributed over a single mode optical fiber several kilometers in length). As such, backscattered light may be measured and further analyzed using signal processing techniques to enable a DAS system to segregate an optical fiber into an array of individual acoustic receivers. More specifically, various pulses of light may be transmitted along the optical fiber, where characteristics of the backscattered light may change due to acoustic vibrations disturbing the casing of the optical fiber. Through DAS processing, the location of these disturbances may be identified.
Keeping with DAS systems, pumping operations may produce various acoustic signals along a wellbore and the adjacent fractures, where the acoustic sensing data depends upon geometrical and physical attributes of the propagating fractures. Accordingly, a quantitative DAS inversion may determine various fracture properties in hydraulic fracture monitoring. For example, a wellbore may be profiled in real time by removing DAS pump noise data and matching acquired data to a forward model regarding pulse propagation in the wellbore and adjacent fractures. Thus, DAS inversion may identify various hydraulic stimulation features such as tubing expansion, fluid-to-fluid interfaces, an adjacent hydraulic fracture, presence of a porous reservoir, and/or an annular compartment. During initial phases of a hydraulic stimulation operation, DAS inversion may determine location information of wireline logging equipment within a wellbore. For example, DAS techniques may verify whether perforating guns and packer-setting devices are disposed at desired depths in the wellbore. In some embodiments, DAS inversion is performed using additional data from distributed temperature sensors (DTS) and/or micro-seismic monitoring techniques.
In certain unconventional formations, for example, an important element that determines whether hydrocarbon recovery is economically viable is the presence of one or more sweet spots in the reservoir. A sweet spot may be generally defined herein as the area within a reservoir that represents the best production or potential for production. In a particular geological region, the sweet spot may be determined based on a lack of ductility, a destruction of internal cohesion, an ability for a rock to deform and fail with a low degree of inelastic behavior, and a rock's capability for self-sustaining fracturing. Likewise, sweet spots may include intervals within organic shales, which possess the highest relative hydrocarbon yield for drilling purposes.
Keeping with sweet spots, sweet spot identification may be used by an unconventional well manager to identify one or more drilling location for unconventional wells. In particular, a sweet spot may be determined with certain reservoir characteristics such as reservoir quality and completion quality based on predicted hydrocarbon data (e.g., predicted well production data for one or more unconventional wells), reservoir data, well log data, seismic data, etc. As such, various technologies may be used to extract resources from unconventional reservoirs at certain sweet spots, such as hydraulic fracturing and horizontal wells.
With respect to proppant systems, a well completion system may include a proppant system. A proppant system may include transfer devices, such as chutes and conveyor belts, for transferring a propping agent (also called simply “proppant”) to a fluid mixing system. Likewise, a proppant system may include one or more proppant storage devices, such as a silo, and a housing. In particular, a silo may use fill ports for acquiring propping agents, which may be subsequently transferred to a fluid mixing system using drain valves and/or outlet ports. The proppant system may then dispense the propping agent to the fluid mixing system for producing a stimulation fluid.
Moreover, a stimulation treatment for a formation may be updated by a an unconventional well manager using a geophysical model. For example, an unconventional well manager may use a geophysical model to perform one or more stimulation simulations using different injection fluid pressure rates, different types of proppants, acid-based treatments and non-acid treatments, etc., to determine a desired stimulation scenario for the formation.
Turning to geosteering, geosteering may be used to position the drill bit or drill string of the drilling system relative to a boundary between different subsurface layers (e.g., overlying, underlying, and lateral layers of a pay zone) during drilling operations. In particular, measuring rock properties during drilling may provide the drilling system with the ability to steer the drill bit in the direction of desired hydrocarbon concentrations. As such, a geosteering system may use various sensors located inside or adjacent to the drill string to determine different rock formations within a well path. In some geosteering systems, drilling tools may use resistivity or acoustic measurements to guide the drill bit during horizontal or lateral drilling. Likewise, a well path of a wellbore may be updated by a control system using a geophysical model. For example, a control system may communicate geosteering commands to the drilling system based on well data updates that are further adjusted by a reservoir simulator using a geophysical model. As such, the control system or an unconventional well manager may generate one or more control signals for drilling equipment (or a logging system may generate for logging equipment) based on an updated well path design and/or a geophysical model.
While
Turning to
In Block 400, various initial unconventional wells are selected for one or more well blocks in accordance with one or more embodiments. In some embodiments, one or more reservoir regions may be demarcated into a number of fields, sub fields, and well blocks for development. For example, an oil and gas field may include a specific number of well blocks (e.g., a 100 discrete sections). A well block may be defined to include various surface facilities (e.g., tie-ins, processing units, pressurizing units, and pipelines) for capitalizing on well production from a projected number of unconventional wells. In particular, an unconventional well manager may determine development costs on a per well block basis. Thus, an initial set of unconventional wells may be selected for providing initial estimates of one or more well blocks. These estimates may be used to fine-tune the amount of well activity in one or more wells to match desired production targets, product targets, and development costs over a desired time period.
Furthermore, well blocks and other well areas may be used to provide a holistic approach to forecasting well activity. Due to the vast size and extent of a master field or petroleum project, well blocks may partition one or more reservoir regions into units with manageable well activity. Once the total well requirements and production profiles are known to achieve desired target levels, corresponding well activity may be distributed into one or more well blocks based on various surface facilities constraints.
In some embodiments, well blocks are assigned to various fluid windows based on subsurface characteristics of the corresponding reservoir region. For example, fluid windows may correspond to different types of reservoir fluids, such as black oils, volatile oils, retrograde gas-condensates, wet gases, and dry gases. By dividing well areas into well blocks based on different fluid windows, the number of wells and production profiles for a particular fluid window may be optimized for the specific type of well block.
In Block 405, target data are obtained for well production and/or hydrocarbon products for one or more well blocks in accordance with one or more embodiments. In some embodiments, for example, target data include desired well production targets for one or more well blocks, desired product targets for one or more plant facilities, and desired development costs. Other examples of target data include specific product yields resulting from specific well activity (e.g., after refining, a particular well block is desired to produce a specific product yield of propane or sales gas).
Additionally, target data may be selected by a user or automatically determined by an unconventional well manager. For example, if a refinery transmits a request to an unconventional well manager for a predetermined amount and type of well production (e.g., light sweet crude oil), an unconventional well manager may determine the target data for various unconventional wells accordingly to satisfy the refinery's request. In another example, an unconventional well manager may calculate volume adjustments for one or more well blocks based on targeted amounts of sales gas. Likewise, target data may include an amount of sales oil.
In Block 410, one or more production constraints are obtained for one or more well blocks in accordance with one or more embodiments. For example, production constraints may include business constraints, such as market prices for gasoline, natural gas, and other hydrocarbon products, as well as surface equipment constraints, such as refining capacity at a particular plant facility. Likewise, production constraints may also include unconventional development constraints, such as the available resources for drilling well, performing hydraulic stimulation operations, and other completion activities. Moreover, production constraints may include the maximum number of wells allowable in a well block, reservoir Area, or subproject. For example, a production constraint may include a list of well blocks within a subproject and their maximum allowable well count for the subproject. Likewise, production constraints may include handling constraints for well production and secondary products, as well as scheduling constraints on actual development of unconventional wells in a well block. Production constraints may also include a predetermined evaluation time (e.g., the start year of a business year), the number of well blocks available for development, and whether the primary hydrocarbon product is oil or gas.
In Block 415, wellstream fluid data are obtained for one or more well blocks in accordance with one or more embodiments. For example, wellstream fluid data may describe various reservoir fluid properties in an unconventional reservoir. Examples of wellstream fluid data may include PVT properties acquired from downhole samples and involve the thermodynamic studies of reservoir fluid with respect to pressure, temperature and its volumetric compositions. Wellstream fluid data may be determined by laboratory experiments performed on the actual samples of the reservoir fluid. Additionally, wellstream fluid data may be based on other reservoir data sources, such as production logging tools, well sensors that include downhole sensors, etc. Wellstream fluid data may also be determined using various equations of state, empirical correlations, and machine-learning models.
In Block 420, production curve data are obtained for one or more selected unconventional wells in a respective well block among one or more well blocks in accordance with one or more embodiments. Production curve data may describe how well production and particular production ratios decline over a predetermined period of time. For example, production curve data may correspond to a condensate production curve, an oil production curve, and a raw gas production curve. Likewise, curves may correspond to a respective unconventional well, a respective well block, a respective field, and/or a respective subproject. Production curves may also correspond to a predetermined period of time, such as a yearly curve type. Production curve data may be obtained based on simulating various well counts to achieve a specific raw well production using the wellstream fluid data.
In Block 425, surface equipment data are obtained for a respective well block in accordance with one or more embodiments. In particular surface accessibility may be an integral of well development, because surface equipment may determine the available transportation of well production from unconventional assets to gathering systems and finally to gas processing plants, refineries, and other plant facilities. As such, surface equipment data may be used to determine potential well activity per well block, thereby enabling a block's distributer to have production limits. In some embodiments, surface equipment data may describe gathering systems, pipeline capacities, plant facilities (e.g., an available refining capacity for converting well production to one or more refined products), etc.
In Block 430, one or more production shrinkage values are determined based on surface equipment data and/or wellstream fluid data in accordance with one or more embodiments. In some embodiments, for example, different equipment and/or chemical processes produce a corresponding amount of hydrocarbon product. Thus, production shrinkage may describe the ratio of a particular product (e.g., gasoline, natural gas condensate, or propane) from an input amount of well production. For well production for different types of unconventional wells, different shrinkage values may apply to the corresponding output products. As such, production shrinkage values may be determined based on an analysis of a wellstream for one or more well blocks as well as analyzing surface equipment. In particular, production shrinkage values may be associated with various gathering systems, midstream equipment, gas processing plants, and refinery processes.
In some embodiments, well production data and/or refined product data may be analyzed at different stages in a well network. Using the analyzed data, shrinkage values may be identified for each stage in the hydrocarbon acquisition and distribution chain from one or more wellheads to commercial outputs (e.g., sales gas and other secondary products).
In Block 440, determine predicted well production data for one or more selected unconventional wells in a respective well block based on one or more production constraints, wellstream fluid data, and/or production curve data in accordance with one or more embodiments. In particular, predicted well production data may be used to calculate various well counts for different well type curves. The predicted well production data for a respective well block can also be used to match desired fuel gas requirements. Moreover, if the predicted well production cannot satisfy fuel gas requirements, an unconventional well manager may increase the number or type of unconventional wells in one or more well blocks.
Furthermore, the predicted well production data may describe a production profile of one or more well blocks for the life of various unconventional wells. The production profile may be based on various field specific inputs such as PVT data, production data from previous years, and gas production curve describing individual wells.
In some embodiments, a user may use various data inputs to generate a required yearly tie-in well count for an unconventional reservoir to meet various sales gas/oil targets. More specifically, target data may be converted to wellhead gas or oil using various production shrinkage factors. A target production goal may be divided by a predetermined time period (e.g., a target each year). Likewise, predicted well production data may describe production profile for primary products (e.g., oil and gas) by multiplying the tie-in well count by the production type curve provided.
In Block 445, predicted refined product data are determined for one or more selected unconventional wells based on one or more production shrinkage values in accordance with one or more embodiments. Based on surface equipment data and the predicted well production, an unconventional well manager may determine predicted amounts of secondary products that can be produced using various plant facilities. For example, predicted refined product data may be determined by multiplying the production ratio of one or more wellheads by the wellhead gas rates to determine actual sales gas. Furthermore, various feed compositions (e.g., (H2S, Co2, C1-C7+) may be analyzed with measured or predicted shrinkage values to determine predicted amounts of various refined products.
In Block 450, predicted development cost data are determined for one or more selected unconventional wells in accordance with one or more embodiments. For example, the level of well activity in a particular well block (e.g., the number of unconventional wells) and a corresponding production profile may determine the design and scope of development in the well block. As such, selected well activity may be used by an unconventional well manager to predict development cost data. Development cost data may include costs associated with well exploration, drilling operations, stimulation operations, other well completion operations, and/or well intervention operations that are used to achieve the desired well production.
In Block 455, predicted well production data, predicted refined product data, and/or predicted development cost data are presented on a display device in accordance with one or more embodiments. For example, a user device may present predicted data in a graphical user interface with respect to different levels of unconventional well activity. Based on the presented data, a user may selection different amounts and/or types of well activity for different well blocks or other well areas.
In Block 460, a determination is made whether predicted well production data, predicted refined product data, and/or predicted development cost data satisfy target data in accordance with one or more embodiments. For example, target data may include one or more predetermined criteria, such as a well production threshold, a refined product threshold, and/or a cost threshold (e.g., for development costs). In some embodiments, for example, if the predicted well production exceeds the desired amount of well production but also exceeds the desired development costs for a particular well block, the number of selected wells in the well block may fail the corresponding predetermined criterion. If a determination is made that the predicted well production, the predicted refined product data, and/or the predicted development cost data satisfy the target data, the process may proceed to Block 475. If a determination is made that the predicted data fail to satisfy the target data, the process may proceed to Block 470.
In Block 470, one or more different unconventional wells are selected for a respective well block in accordance with one or more embodiments. In some embodiments, for example, an unconventional well manager distributes various unconventional wells among different well blocks and/or fields in order to achieve desired target data. For example, the selected number of unconventional wells for development based be based on a maximum allowable well count per well block. This maximum allowable well count may be based available resources for well development (e.g., available oilfield services for stimulation and completion operations) as well as allowable processing capacity with gathering systems and adjacent plant facilities.
In Block 475, a well development plan is determined for one or more well blocks based on selected unconventional wells and/or predicted development cost data in accordance with one or more embodiments. Based on selecting an amount of well activity for one or more well blocks to satisfy target data and any other predetermined criteria, an unconventional well manager may automatically determine a well development plan to implement the corresponding well activity. For example, the well development plan may include scheduled drilling operations, hydraulic stimulation operations, injection well operations, well completion operations, and/or well intervention operations to achieve the amount of well activity. An unconventional well development plan may also include shut-in operations, maintenance operations, and operations to initiate production. Likewise, the unconventional well development plan may also manage various plant facility operation (e.g., managing refinery processes based on predicted well production) and surface midstream operation (e.g., maintenance for a gathering system). Turning to
In Block 480, one or more commands are transmitted to one or more control systems based on a well development plan, predicted well production data and/or predicted refined product data in accordance with one or more embodiments. For example, commands, such as control signals, may be transmitted over a well network connecting multiple well sites, plant facilities, and other surface equipment, to implement a well development plan. For example, commands may adjust production settings at various wells or include shut-off commands for terminating production operations. Commands may also be used to initiate or increase production operations at a specific well based on predicted well production data or predicted refined product data. In some embodiments, commands are used to trigger one or more stimulation operations, such as to implement a desired amount of well activity in one or more well blocks.
In Block 490, one or more hydraulic stimulation operations are performed for one or more selected unconventional wells in a respective well block based on a well development plan in accordance with one or more embodiments. For example, the hydraulic stimulation operations may be similar to one or more hydraulic stimulation operations described above in
In some embodiments, an unconventional well manager may various economic evaluations of one or more well blocks. These economic evaluations may identify various well activities and potential well production using a granular approach. As such, the unconventional well manager may match program development components to well production targets and refined product targets. Since unconventional assets may be capital intensive, the unconventional well manager may evaluate and breakdown a massive unconventional well reservoir development into smaller sub-fields and well blocks for easier optimization. In some embodiments, for example, the unconventional well manager may obtain various user selections regarding different levels of well activity for economic evaluations and forecasting program production profiles per development area. By determining development cost estimates and projected revenues per product stream (e.g., sales gas, crude oil, propane, etc.), the unconventional well manager may optimize development of unconventional reservoir assets. More specifically, the unconventional well manager may iterate through multiple cases and scenario with different development times and adjust the number of unconventional wells in different fields and well blocks undergoing analysis.
Embodiments may be implemented on a computer system.
The computer (602) can serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer (602) is communicably coupled with a network (630). In some implementations, one or more components of the computer (602) may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments).
At a high level, the computer (602) is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer (602) may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers).
The computer (602) can receive requests over network (630) from a client application (for example, executing on another computer (602)) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer (602) from internal users (for example, from a command console or by other appropriate access method), external or third-parties, other automated applications, as well as any other appropriate entities, individuals, systems, or computers.
Each of the components of the computer (602) can communicate using a system bus (603). In some implementations, any or all of the components of the computer (602), both hardware or software (or a combination of hardware and software), may interface with each other or the interface (604) (or a combination of both) over the system bus (603) using an application programming interface (API) (612) or a service layer (613) (or a combination of the API (612) and service layer (613). The API (612) may include specifications for routines, data structures, and object classes. The API (612) may be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer (613) provides software services to the computer (602) or other components (whether or not illustrated) that are communicably coupled to the computer (602). The functionality of the computer (602) may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer (613), provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer (602), alternative implementations may illustrate the API (612) or the service layer (613) as stand-alone components in relation to other components of the computer (602) or other components (whether or not illustrated) that are communicably coupled to the computer (602). Moreover, any or all parts of the API (612) or the service layer (613) may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.
The computer (602) includes an interface (604). Although illustrated as a single interface (604) in
The computer (602) includes at least one computer processor (605). Although illustrated as a single processor (605) in
The computer (602) also includes a memory (606) that holds data for the computer (602) or other components (or a combination of both) that can be connected to the network (630). For example, memory (606) can be a database storing data consistent with this disclosure. Although illustrated as a single memory (606) in
The application (607) is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer (602), particularly with respect to functionality described in this disclosure. For example, application (607) can serve as one or more components, modules, applications, etc. Further, although illustrated as a single application (607), the application (607) may be implemented as multiple applications (607) on the computer (602). In addition, although illustrated as integral to the computer (602), in alternative implementations, the application (607) can be external to the computer (602).
There may be any number of computers (602) associated with, or external to, a computer system containing computer (602), each computer (602) communicating over network (630). Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer (602), or that one user may use multiple computers (602).
In some embodiments, the computer (602) is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile “backend” as a service (MBaaS), serverless computing, and/or function as a service (FaaS).
Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.
