Martingale control of production for optimal profitability of oil and gas fields

Abstract

A computer-aided lean management (CALM) controller system recommends actions and manages production in an oil and gas reservoir/field as its properties and conditions change with time. The reservoir/field is characterized and represented as an electronic-field (“e-field”). A plurality of system applications describe dynamic and static e-field properties and conditions. The application workflows are integrated and combined in a feedback loop between actions taken in the field and metrics that score the success or failure of those actions. A controller/optimizer operates on the combination of the application workflows to compute production strategies and actions. The controller/optimizer is configured to generate a best action sequence for production, which is economically “always-in-the-money.”

Description

BACKGROUND

This disclosure relates to systems and methods for managing oil and gas fields. In particular, the invention relates to computer-aided lean management (“CALM”) of hydrocarbon production from oil and gas fields or subsurface reservoirs.

Computer-aided management techniques have been beneficially used to increase efficiencies in complex product manufacturing enterprises such as aircraft manufacturing. Computer-aided lean management (CALM) techniques involve a feedback loop between actions taken on the production floor and the return of metrics that score the success or failure of those actions.

In the context of hydrocarbon resources, an “e-Field” is an integrated asset model of the physical equipment and electronic infrastructure, for real-time remote monitoring and control of gas, oil, and water production in ultra deepwater and unconventional gas fields. See, e.g., Thomas et al. U.S. Pat. No. 6,266,619 (“Thomas”).

The tracking of fluid drainage over time (called “4D”) is a modern development aimed at improving reservoir monitoring. 4D has introduced several powerful new observational tools into the development engineering arsenal, such as time-lapse seismic differencing, fiber-optic monitoring arrays in casing, and downhole sensors of many types.

This 4D application holds great promise as the keystone of a new, integrated reservoir management strategy that is able to image changes not only within a reservoir but also within the stack of reservoirs that make up most of the oil and gas fields of the world today.

Yet the industry is only just developing the controller logic for many components of 4D monitoring. For example, 4D seismic monitoring is still centered on reacquisition using 3D methodologies that are hard to reproduce or duplicate exactly. Consequently, field operators concentrate on seismic reprocessing and reinterpretation, instead of the differencing of time-lapse data itself.

In addition, conventional seismic modeling is 1D and 2D, rather than 3D like the earth. Further, seismic modeling is usually acoustic rather than elastic, which is more expensive. To add to the simplification, seismic modeling analysis is built around one reservoir at a time, instead of the system of stacked reservoirs as an integrated whole.

Anderson et al., U.S. Pat. No. 6,826,483, which is incorporated by reference in its entirety herein, describes a 4D system and method for managing and optimizing data handling and analysis over a period of time relative to a characterization of the state, location, and quantity of fluids within a subterranean petroleum reservoir. The system and method are based on a networked operating framework (“OF”) that sources, then integrates, multi-vendor scientific, business, and engineering applications and data sets. The OF manages, versions (i.e. times), and coordinates execution of multiple applications. It handles the trafficking of data between applications; updates geospatially aligned earth and reservoir models; and orchestrates the outcomes through optimization loops. The OF infrastructure (referred to herein as a “middleware framework”) allows for very large volume data sets to be configured and efficiently transported among disparate geological, geophysical, and engineering software applications, the looping through of which is required to determine accurately the location over time of the oil and gas within the reservoir relative to the surrounding water in the rock matrix. The OF infrastructure includes software to track the progress of the workflow throughout the history of computation around the loop, including the versioning (i.e., keeping track of, accounting for, and/or recording changes) over time of the various data and results.

Anderson's computational operating framework (OF) allows for the seamless and rapid feedback between and among the many and varied software applications and data streams that are required for modern reservoir management. This computational operating framework is missing from prior art e-field and smart-field controllers.

Anderson et al. U.S. patent application Ser. No. 11/349,711 provides systems and methods for computer-aided lean management (CALM) of enterprises. A stochastic controller system is used to optimize decision making over time. A unified reinforcement learning algorithm is implemented to treat multiple interconnected operational levels of enterprise processes in a unified manner. A forward model of the enterprise processes is used to train the unified reinforcement learning algorithm to generate optimal actions over time.

The stochastic controller system can be configured to carry out remote sub-sea decisions in real time, affecting the form and timing of gas, oil, and water production in ultra deepwater. An integrated reservoir asset and production model is developed. The model may include production constraints based, for example, on skin damage and water coning in wells. The stochastic controller is trained to generate flexible production/injection schedules that honor production constraints and produce exemplary field production shapes. The flexible production/injection schedules can be optimized on the basis of total economic value increase (real option value+NPV) by the controller.

Reservoir evaluation and characterization generically (including but not limited to that using seismic, non-seismic, and hybrid data analysis) will be referred to herein as “SeisRes OF.” Further, seismic/reservoir modeling integration of CALM software will be referred to herein as “4D SeisRes,” or “4D Seismic Reservoir Management.”

Consideration is now being given to an implementation of a CALM controller system for 4D Seismic Reservoir Management, which is focused on the need to maximize profitability of the whole enterprise through all times and under all uncertainties. A desirable CALM controller will integrate observed 4D seismic differences with a continuously running reservoir simulator to understand the production pathways of fluid withdrawal in each field.

SUMMARY

This disclosure provides “real options” management of hydrocarbon production from wells through feedback of economic and reservoir conditions to an optimizer that varies the chokes and production profiles of well producers and injectors simultaneously to manage production through time for optimal profitability.

The optimizer is integrated with a multi-component 4D Seismic Reservoir Management system, which includes verified seismic differencing schemes, both amplitude and time-shift imaging to bring out the 4D changes over time, petrophysical and rock mechanical studies and follow-up, and continuous monitoring from sensors embedded in the sea floor and wellbores.

A computer-aided lean management (CALM) controller system for managing oil and gas field production with a view to optimize profitability is coupled with an e-field representation of the field (e.g., 4D SeisRes). The multi-component 4D Seismic Reservoir Management system includes a plurality of applications describing a multiplicity of dynamic and static e-field properties and conditions. The system monitors the reservoir conditions, tracks actions taken, and uses machine-learning continuously in real time. The workflow of these applications is combined in a feedback loop. A learning model continuously recomputes optimal solutions, strategies or actions to keep the next actions “always-in-the-money.”

The system can generate or predict in-the-money actions even as reservoir/field properties and conditions change with time. Implementing such actions can advantageously increase profitability of the reservoir over its lifetime.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features of the disclosed subject matter, its nature, and various advantages will be more apparent from the following detailed description of the preferred embodiments and the accompanying drawings, wherein like reference characters represent like elements throughout, and in which:

FIG. 1 is an illustration of a feedback loop in an exemplary implementation of the CALM Martingale controller system for 4D seismic reservoir management for optimal profitability in accordance with the principles of the present invention.

FIG. 2 is an illustration of a web-based action tracker (“Active Notebook”), which records and/or displays actions within any of the many applications of component subsystems of the system of FIG. 1, in accordance with the principles of the present invention.

FIG. 3 is an illustration of reservoir horizons (tops and bottoms) mapped and rendered in a stacked reservoir model, which is generated by the system of FIG. 1 and displayed on the web-based action tracker of FIG. 2, in accordance with the principles of the present invention.

FIG. 4 is an illustration of an exemplary automated meshing system which creates a 3D grid by using a topological representation of 2D horizons and reservoir tops for input into a reservoir simulator used in the system of FIG. 1, in accordance with the principles of the present invention.

FIG. 5A is an exemplary web-display of a producing reservoir's real-time drainage simulated by the system of FIG. 1, in accordance with the principles of the present invention.

FIG. 5B is another exemplary web-display of a producing reservoir's drainage over time simulated by the system of FIG. 1, in accordance with the principles of the present invention.

FIG. 6 is a schematic illustrating the operation of the CALM Martingale 4D SeisRes controller system, in accordance with the principles of the present invention.

FIG. 7 is an illustration of the movement over time of oil toward two wells plotted on a reservoir horizon.

FIG. 8 is an illustration of an exemplary output of the CALM Martingale 4D system optimizer of FIG. 1, when permeability is varied from node to node of the reservoir multimesh to match flow rates of oil and gas by minimizing errors in predicted vs. observed production histories of the well and changes in 4D seismic amplitude over the 15-month period, in accordance with the principles of the present invention.

DESCRIPTION

This disclosure provides a CALM controller system for managing oil and gas field production with a view to optimize profitability. The CALM controller system for 4D Seismic Reservoir Management is designed to focus on the need to maximize profitability of the whole at all times and under all uncertainties. The CALM controller system integrates the observed 4D seismic differences with a continuously running reservoir simulator to understand the production pathways of fluid withdrawal in each field. The CALM controller system includes a feedback loop between actions taken in the field and the return of metrics that score the success or failure of those actions. This feedback is fed into a model that continuously recomputes optimal solutions, strategies or actions to keep the next actions “always-in-the-money.” Such a feedback loop in control system simultaneously optimizes both economic value and operational aspects of “e-fields” is called a “Martingale” controller.

The CALM Martingale controller system (“CALM Martingale 4D SeisRes controller system”) disclosed herein is an implementation of the general stochastic controller described in Anderson et al. U.S. patent application Ser. No. 11/349,711. The system integrates diverse and multiple software applications and models using a feedback loop.

FIG. 1 shows a feedback loop 100 in an exemplary implementation of the CALM Martingale controller system for 4D seismic reservoir management for optimal profitability. The system generates a strategy (i.e., actions) for optimal profitability of the reservoir through time. The CALM controller integrates the observed 4D seismic differences 110 with a continuously running reservoir simulator 120 to understand the production pathways of fluid withdrawal in each field. Advantageously, the CALM system maximizes profitability even with extreme price fluctuations and geological uncertainties.

Exemplary feedback loop 100 includes the following workflow combinations:

• • 1. 4D analysis of time-lapse seismic changes (e.g., amplitude, compressional and shear velocity) for at least two, but as many as practical, 3D seismic volumes acquired at different times during the production history of a field, and their time-depth conversion, normalization, and differencing;
  • 2. Well log analysis looking for time-lapse changes in the logs run over several different time intervals in wells and their time-depth conversion;
  • 3. Reservoir characterization of stacked reservoirs using geostatistical co-kriging;
  • 4. Exporting of all data, with time-stamps, into the same earth model;
  • 5. 3D fluid flow simulation, preferably using finite element modeling;
  • 6. 3D seismic modeling, preferably elastic rather than acoustic, to generate synthetic seismic cubes to match the 4D seismic observation time steps;
  • 7. Differencing of 4D model vs. 4D observed seismic data (e.g., difference between model and observed data for a given time);
  • 8. Analysis of the difference-of-the-differences between the model and observed results (e.g., the difference between the predicted-observed differences at two times);
  • 9. Optimization that identifies changes in physical properties of the reservoirs that are required to match fluid withdrawal, pressure changes, and seismic differences as closely as possible; and
  • 10. A continuous “feedback loop” to workflow 1 above so that the computation is dynamic. The computations can be continuous (e.g., 24 hrs/7 days a week) until the field is abandoned.

The CALM Martingale 4D SeisRes controller system is designed to perform the computational tasks of interacting 3D seismic modeling 130 with statistical reservoir characterization 140, 4D observed seismic differences, a finite element reservoir simulator 120, and properties derived from seismic inversion and migration codes 150.

For this purpose, an exemplary implementation of the CALM Martingale 4D SeisRes controller has an extensible operating framework (OF) that enables the interpretation workflow to move easily among various vendor applications needed to complete feedback loop 100 (FIG. 1). The interpretation workflow may include geological and geophysical interpretations, and engineering implementations required for optimal performance by modern e-field asset teams.

FIG. 2 shows the architecture of OF 200 for the exemplary system implementation of CALM Martingale 4D SeisRes controller. OF 200 includes major geological, geophysical, and engineering components or subsystems (e.g., Rock Properties, 4D seismic Model, 3D Reservoir simulator, 4D Seismic changes, etc.), which are managed simultaneously.

In OF 200, all actions within any of the many applications of the system or component subsystems are recorded within the OF using a web-based action tracker (hereinafter “Active Notebook”). Like a laboratory scientist's lab book, Active Notebook allows any previous experiment to be redone.

OF 200 also includes a vendor-neutral data model with a persistent input/output data repository 210 is required because the data sources for the various users' favorite applications are not likely to be kept in the same data management systems. Rapid reviews of present and previous analyses can then be quickly and easily reviewed via versions stored in the data repository.

OF 200 may provide user-selectable access to various vendors' applications using automated “wrappers” (FIG. 2). For example, OF 200 may allow a user to choose between popular reservoir simulators (e.g., Schlumberger (Eclipse) and Halliburton (VIP) simulators, etc.) at the same time. The wrappers enclose the vendor applications and automatically manage the connectivity, data trafficking, and versioning of inputs and outputs.

OF 200 includes an “event handling” mechanism to make applications run asynchronously (i.e., in parallel, but not necessarily at the same time). The workflow is parsed among the several parallel applications simultaneously and distributed to the client/server network, and then reassembled as it is completed. With this feature, OF 200 does not have to wait for one program to finish before beginning another.

In exemplary embodiments of the implementation of the CALM Martingale 4D SeisRes controller, OF 200/data repository 210 is configured to have a rich set of reusable, extensible “containers” to hold engineering, geological, and geophysical data so that new applications and data types can be added to the OF management system easily and quickly.

In an implementation of the CALM Martingale 4D SeisRes controller, OF 200 includes an automated meshing system 220 to create the framework needed by the reservoir simulator to build its model from any set of stacked horizons or other geological interpretations such as scanned maps and charts. FIG. 3 shows, for example, OF 200 displays of reservoir horizons (tops and bottoms) mapped and rendered in a stacked reservoir model.

Automated meshing system 220 (e.g., “MultiMesh” by IBM) is advantageously “topological” so that whatever the grid requirements of an application are, its 3D connectivity can be quickly computed in finite difference, finite element, upscaled, or downscaled versions as needed (FIG. 4).

Further, OF 200 may be provided with a common data viewer (e.g., a web-based visualization system) that crosses applications. The user can then communicate the visualization of progress of his computer simulations over the web as colleagues around the world manipulate the images in real time as they are recomputing (FIGS. 5A and 5B).

In addition to the OF 200 components discussed above, the CALM Martingale 4D SeisRes controller system includes an optimizer, which provides parameter optimization services. Since the CALM Martingale 4D SeisRes controller system is implemented as a loosely coupled, component-based system, the need for parameter estimation varies from application to application and vendor to vendor. The optimizer contains a set of tools that can be deployed at any time and any place within SeisRes OF 200. This feature effectively provides the user with an “optimization laboratory or workbench.”

The “optimization workbench” allows the user a selection of options, including hybrids that combine algorithms of different types, to produce the most appropriate solutions. In practice, the technical goal is to quickly implement sub-optimization loops to facilitate the optimization process for the 4D seismic reservoir simulation.

Consider application of the CALM Martingale 4D SeisRes controller system to an exemplary problem of tracking drainage of oil from a reservoir by two wells over a 15-month period (FIG. 5A). For this problem, an exemplary optimizer may include three components: optimization solvers, forward simulation wrappers, and simulation data converters, each of which must be developed separately for reservoir property characterization, reservoir simulation, petrophysical property characterization, and 3D seismic simulation. The optimizer converges on a single “best approximation” result that simultaneously solves for permeability variations and flow rate changes, with error estimates for each. FIG. 6 schematically shows the operation of the CALM Martingale 4D SeisRes controller system in this application. The controller system monitors the reservoir conditions, tracks actions taken, and uses machine learning continuously in real time, to generate a best action sequence. The best action sequence, for example, is constrained, for example, to ensure maximum profitability of the reservoir at a give time or cumulatively.

FIG. 5B is an exemplary web-display of a producing reservoir's drainage over time as simulated the CALM Martingale 4D SeisRes controller system. The figure shows a 3×3 grid of several amplitude mapping plots (e.g., observed, predicted, observed-observed difference, observed-predicted difference, predicted-predicted difference, and observed difference-predicted difference plots) displayed in the web-based viewer.

The CALM Martingale 4D SeisRes controller system provides a modern, real-time reservoir management system that is able to characterize multiple, sequential 4D seismic surveys; seismic attribute volumes that vary with offset; many repeated well logs of different types and vintages; geostatistically-derived data volumes in both spatial and “stratigraphic” grids; 3D fluid saturation volumes and fluid-flow maps; fluid-interface monitors, multiple horizons and fault surfaces, and other data types that are at present only on the R&D drawing board (FIG. 7).

FIG. 7 shows the movement over time of oil toward two wells plotted on a reservoir horizon. The four snapshots (a)-(d) of drainage patterns were made by the 4D SeisRes system from interpolation using the reservoir simulator calibrated by 3D seismic surveys made at (a) and (d) 15 months apart.

FIG. 8 shows exemplary output of the CALM Martingale 4D system optimizer when permeability is varied from node to node of the reservoir multimesh to match flow rates of oil and gas by minimizing errors in predicted vs. observed production histories of the well and changes in 4D seismic amplitude over the 15-month period.

The CALM Martingale 4D SeisRes controller system takes a global view of all these different types of data, which are spatially registered with respect to one another in the local real-world coordinate system and rendered in a variety of modes so that the interrelationships can be perceived. This global view is a key to martingale control of an e-field, so that CALM Martingale 4D SeisRes controller system will deliver profitability that is “always-in-the-money.”

In accordance with the present disclosure, software (i.e., instructions) for implementing the aforementioned controllers and systems can be provided on computer-readable media. It will be appreciated that each of the steps (described above in accordance with this invention), and any combination of these steps, can be implemented by computer program instructions. These computer program instructions can be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions, which execute on the computer or other programmable apparatus create means for implementing the functions of the aforementioned controllers and systems. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the functions of the aforementioned controllers and systems. The computer program instructions can also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions of the aforementioned innervated stochastic controllers and systems. It will also be understood that the computer-readable media on which instructions for implementing the aforementioned controllers and systems are be provided, include without limitation, firmware, microcontrollers, microprocessors, integrated circuits, ASICS, and other available media. It will be understood that the foregoing is only illustrative of the principles of the invention, and that various modifications can be made by those skilled in the art, without departing from the scope and spirit of the invention, which is limited only by the claims that follow.

Claims

1. A computer-aided lean management (CALM) controller system for managing production actions in an oil and gas reservoir/field as its properties and conditions change with time with a view to increase profitability of the reservoir over its lifetime, wherein the oil and/or gas reservoir is characterized and represented as an electronic-field (“e-field”), the system comprising: a plurality of applications describing a multiplicity of dynamic and static e-field properties and conditions, each application having a workflow,wherein the plurality of application workflows are combined in a feedback loop between actions taken in the field and metrics that score the success or failure of those actions in the field; anda martingale controller that operates on the combination of the application workflows and continuously computes production strategies and physical actions on an oil and gas reservoir/field, wherein the martingale controller is configured to generate a best temporal action sequence for production in which a future-looking real option value representing one of the actions is used as at least one basis for determining the best action sequence thereby ensuring increased profitability of the reservoir over its lifetime.

2. The system of claim 1, wherein the feedback loop connects the following workflows: 4D analysis of time-lapse seismic changes for at least two 3D seismic volumes acquired at different times during the production history of the field, and their time-depth conversion, normalization, and differencing;well log analysis looking for time-lapse changes in the same logs run over several different time intervals in wells and their time-depth conversion;reservoir characterization of stacked reservoirs using geostatistical co-kriging;exporting of all data, with time-stamps, into a same earth model;3D fluid flow simulation;3D seismic modeling to generate synthetic seismic cubes to match the 4D analysis of time-lapse seismic changes;differencing of 4D model vs. 4D observed seismic data;analysis of the difference-of-the-differences between the model and observed results; andoptimization that identifies changes in physical properties of the reservoirs that are required to match fluid withdrawal, pressure changes, and seismic differences.

3. The system of claim 1 comprising interacting 3D seismic modeling with statistical reservoir characterization applications, a 4D observed seismic differences application, a finite element reservoir simulator, and seismic inversion and migration codes.

4. The system of claim 3, comprising an operating framework (OF) that enables interpretation workflow to move easily among diverse vendor applications needed to complete feedback loop, wherein the interpretation workflow includes geological and geophysical field interpretations, and engineering implementations required for optimal performance by electronic oil field asset teams.

5. The system of claim 4, wherein the OF comprises a rock property data application, a 4D seismic Model, a 3D reservoir simulator, and a 4D seismic change application.

6. The system of claim 4, wherein all actions within any component application of the OF are recorded using a web-based action tracker.

7. The system of claim 4, wherein the OF includes a vendor-neutral data model with a persistent input/output data repository.

8. The system of claim 4, wherein the OF is configured to provide user-selectable access to diverse vendors' applications using automated wrappers.

9. The system of claim 4, wherein the OF comprises an event handling mechanism to make OF component applications run asynchronously.

10. The system of claim 4, wherein the OF comprises an automated meshing application to create a framework needed by the reservoir simulator to build its model from any set of stacked horizons or other geological interpretations.

11. The system of claim 1 comprising an optimizer, which provides parameter optimization services.

12. The system of claim 11 wherein the optimizer comprises a set of tools that can be deployed at any time and any place within the system.

13. The system of claim 12 wherein the optimizer comprises optimization solvers, forward simulation wrappers, and simulation data converters, each of are deployed separately for reservoir property characterization, reservoir simulation, petrophysical property characterization, and 3D seismic simulation.

14. The system of claim 11 wherein the optimizer is configured to converge on a single “best approximation” result that simultaneously solves for permeability variations and flow rate changes in the field.

15. The system of claim 11 that is configured to characterize multiple, sequential 4D seismic surveys; seismic attribute volumes that vary with offset; many repeated well logs of different types and vintages; geostatistically-derived data volumes in both spatial and “stratigraphic” grids; 3D fluid saturation volumes and fluid-flow maps; fluid-interface monitors, multiple horizons and fault surfaces and other data types.