Systems and methods for subsurface oil recovery optimization.

(54) Title: SYSTEMS AND METHODS FOR THE OPTIMIZATION OF RECOVERY OF SUBSURFACE OIL. (54) Title: SYSTEMS AND METHODS FOR SUBSURFACE OIL RECOVERY OPTIMIZED.

(57) Summary

Systems and methods for the optimization of secondary and / or tertiary recovery of subsurface oil based on optimization analysis of any short-term, medium-term or long-term of the selected areas, wells, patterns / groups and / or fields.

(57) Abstract

Systems and methods for subsurlace secondary and / or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns / clusters and / or fields.

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HCRCTASU DE BCWOMÍ *

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Institute

Mexican Property

Industrial

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PATENT TITLE NO. 337098

Headlines):

Home:

Denomination:

Classification:

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LANDMARK GRAPHICS CORPORATION

2107 Citywest Blvd. Bldg. 2, Houston, Texas, 77042, USA

SYSTEMS AND METHODS FOR THE OPTIMIZATION OF RECOVERY OF SUB-SURFACE OIL.

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07/01/2002, 07/15/2004, 07/28/2004 and 09/07/2007); articles 1, 3, 4, 5, section V, subsection aj; 16 sections I and III and 30 of the Organic Statute of the Mexican Institute of Industrial Property (DOF 12/27/1999, amended on 10/10/2002, 07/29/2004, 08/04/2004 and 09/13/2007); 1 ·, 3 'and 5 ° subsection a) of the Agreement that delegates powers to the Deputy Directors General, Coordinator, Divisional Directors, Holders of Regional Offices, Divisional Deputy Directors, Departmental Coordinators and other subordinates of the Mexican Institute of Industrial Property. (DOF 12/15/1999, amended on 02/04/2000, 07/29/2004, 08/04/2004 and 09/13/2007).

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Issue Date: February 11, 2016

THE DIVISIONAL DIRECTOR OF PATENTS

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NAHANNY CANAL REYES

Arenal No. 550, Floor 1,

Col. Pueblo Santa María Tepepan, Xochimilco, CP 16020,

Mexico City

Tel í55) 53 34 07 00 www.grtpi.qob.mx llllllllllllll

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MX / 2016/13092

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SYSTEMS AND METHODS FOR RECOVERY OPTIMIZATION

SURFACE OIL

FIELD OF THE INVENTION

The present invention generally relates to systems and methods for optimizing subsurface oil recovery. More particularly, the invention relates to optimization of secondary and / or tertiary recovery of subsurface oil based on optimization analyzes of either short-term, medium-term, or long-term areas, wells, patterns / groups, and / or selected fields.

BACKGROUND OF THE INVENTION

Different systems and methods are well known to maximize secondary and / or tertiary recovery of subsurface oil. Current systems to maximize secondary and / or tertiary recovery are generally based on many steps, on different systems, and on software tools that users need to configure and manage themselves. This is a manual process, where the user will create a numerical analysis model of the deposit, will run the model with few decisions and / or different operating parameters, will analyze the

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results and you will choose the best answer. The non-automated process often requires running multiple applications, which are not integrated, to get results that will be integrated. As a result of the different applications required, a significant amount of reformatting data may be required between applications, creating additional work and the potential for error. Furthermore, as the process is carried out manually in numerous publications, there are no traces of electronic review for further review. This can be further complicated since analysis tools are generally generic and not designed to integrate data and to provide and evaluate simulations according to variable criteria.

Current systems provide very little feedback on model quality and verification to ensure that the results are realistic. They do not provide interactive graphical feedback to the user at different levels of field operations and do not provide true optimization tools and decision support.

Nor do they take advantage of the true value of the field's real-time data. As a result, current systems are manual, laborious, and require data transfer from one system to another while requiring users to verify that the output of a system is usable like

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entry to another system. These deficiencies in current systems mean that the number of people who can do this type of work is very limited. As a result, this process is carried out by a limited number of experts within an organization. With a currently available set of tools, even these experts take a long time to complete the process and are prone to errors due to the manual nature of the process.

As a result of the limitations of current systems, users generally do not see multiple scenarios to account for potential uncertainties in the numerical model of the underlying deposit. Nor do users exhaustively use optimization technologies to analyze, classify, and choose the best development operations to increase secondary and / or tertiary oil recovery. This often excludes users from addressing uncertainties in a reservoir model by periodically reevaluating selected scenarios based on data such as historical reservoir performance, patterns, wells, and / or zones or other data. Furthermore, in addition to all the limitations listed above, current systems do not provide good tools to allow a user to update a model, or series of

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INl / .u models. These difficulties in generating a model serve as an impediment to the generation of subsequent updates.

Nor do current systems address overall field performance or the effectiveness of secondary or tertiary recovery processes. Practitioners of current processes will generally recognize that sweep efficiency is an important measure of the effectiveness of the recovery process. Sweep efficiency can be calculated at different locations in a field and at different scales. For example, sweep efficiency could be calculated locally near a well, at a zone level, between two wells, at a standard level, at a field level, and at different intermediate levels. Currently, there is no good method to measure or calculate the indicators of the state of sweep efficiency. There is also no integrated system and method for simulating and optimizing well production simultaneously at different scales or classifications from field levels to equipment.

BRIEF DESCRIPTION OF THE INVENTION

The present invention therefore meets the above needs and overcomes one or more deficiencies in the current art by providing systems and methods for optimization of

MEXICAN INSTITUTE

OF THE CV PROPERTY—⁵** · '/' 'eGÁÍ INDUSTRIAL tertiary oil based on any short-term,^- Term of the zones, wells, secondary recovery and / or optimization analysis medium or long patterns / groups and / or selected fields.

In one embodiment, the present invention includes a method for optimizing long-term oil recovery, comprising: i) selecting one or more zones, wells, patterns / groups or fields where said one or more zones, wells, patterns / Groups or fields are selected from a sweep efficiency status display, comprising one of a current sweep efficiency status display and one of multiple predicted sweep efficiency status displays; ii) show multiple optimization scenarios and corresponding actions for the optimization of said one or more zones, wells, patterns / groups or fields selected during an evaluation of a plan for the development of a field;

iii) select one or more of the optimization scenarios and show each corresponding action; iv) select a prediction date for each selected optimization scenario; and v) show said one or more optimization scenarios, the effect of each corresponding action in said one or more zones, wells, patterns / groups or fields selected on the prediction date, and a plan of

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Updated field development using a computer system, the updated field development plan is displayed for a field with a respective net present value calculation and projected production parameters.

In another embodiment, the present invention includes a program-carrying device for carrying computer-executable instructions for optimizing long-term oil recovery. The instructions are executable to implement: i) select one or more zones, wells, patterns / groups or fields where said one or more zones, wells, patterns / groups or fields are selected from a sweep efficiency status display, which it comprises one of a current scan efficiency status display and one of multiple predicted scan efficiency status displays; ii) show multiple optimization scenarios and corresponding actions for the optimization of said one or more zones, wells, patterns / groups or fields selected during an evaluation of a plan for the development of a field;

iii) select one or more of the optimization scenarios and show each corresponding action; iv) select a prediction date for each selected optimization scenario; and v) show said one or more optimization scenarios, the effect of each corresponding action on

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Said one or more zones, wells, patterns / groups or fields selected on the prediction date, and an updated field development plan, the updated field development plan is displayed for a field with a respective net present value 5 calculation. and parameters of projected production.

Additional aspects, advantages and embodiments of the invention will become apparent to those skilled in the art from the following description of the different embodiments and related drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described below with reference to the accompanying drawings in which like elements are referenced by like reference numbers, and in which:

Figure 1 illustrates a general process for optimizing subsurface oil recovery in accordance with the present invention.

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modality of a method to carry out step 216 of the

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Figure 2.

FIG. 4 is a flow chart illustrating an embodiment of a method for carrying out step 220 of the

Figure 2.

FIG. 5 is a flow chart illustrating an embodiment of a method for performing step 224 of the

Figure 2.

Figure 6 is a block diagram illustrating one embodiment of a system for implementing the present invention.

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Alternative illustrating step 412 in Figure 4.

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Figure 14 is a graphical user interface illustrating step 422 of Figure 4.

Figure 15 is a table illustrating the optimization examples provided by the invention.

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eg using present levels

DETAILED DESCRIPTION OF THE INVENTION

The subject matter of the present invention is described specifically, however, the description itself is not intended to limit the scope of the invention. The subject can therefore also be incorporated in other ways, to include different steps or combinations of steps similar to those described in this document, in conjunction with other present or future technologies. Furthermore, although the term step can be used in this document to describe different elements of the methods used, the term should not be interpreted as implying any particular order among the different steps disclosed in this document unless expressly limited otherwise. by the description to a particular order. While the following description refers to the oil and gas industry, the systems and methods of the present invention are not limited thereto and can also be applied in other industries to achieve similar results.

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The present invention includes systems and methods to optimize oil recovery, by reducing unwanted fluid / gas production, reducing repair downtime, reducing surplus oil and gas, and maximizing present value through optimization. of the injection and production profiles. The systems and methods therefore consider the intelligent manipulation of subsurface displacement profiles;

surface and facility optimization constraints, well intervention / re-completion designs, and dynamic field development planning through decisions to drill and design new production / injection / observation wells.

Systems and methods carry out all permutations and combinations with surveillance, diagnostics, and optimization from a micro to a macro scale ranging from equipment level to zone level to well level to a pattern / group level to , finally, the tank / field level. The systems and methods allow the user to carry out present and / or predictive diagnostics in the field and / or state of sweep efficiency, as well as advise the user of the optimal optimization actions for short, medium and long time frames. term. The systems and methods allow the user to carry out

Τ&gt;) Τ Interactively comparative scenarios of what would happen if (war games) with previously recommended optimization actions, generate appropriate business cases, and therefore take and implement appropriate optimization actions that help maximize recovery of oil and economic value.

The systems and methods use real-time surveillance field data to provide advanced value for integrated asset management, providing automated consulting for multiple short, medium and / or long-term wells / patterns and optimization at the field level. The systems and methods enable staff to perform predictive analytics on the effect of selected optimization actions, and deliver an intuitive user interface for enhanced collaborative decision-making among asset, warehouse, operations, and production staff. The systems and methods, therefore, or the need for laborious simulation and optimization in separate actions.

In short, the systems and methods enable the monitoring of the subsurface state of a production field and provide automated advice on proactive tank diagnostics with tangible optimization actions, thus allowing the predicted analysis in the

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optimization actions of the proposed deposit.

Method Description

Referring now to Figure 1, a general process 100 for recovery of subsurface oil in accordance with the present invention is illustrated.

In step 102, process 100 identifies the current state of the field. A modality of a method to identify

<td>the state</td><td>from the field</td><td>today</td><td>is illustrated by the</td><td>step 202 in</td><td>the</td>

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At step 106, process 100 diagnoses the state of the field for today and the future, which may include identifying and detecting surpassed and unscanned oil points using a mobile water saturation function. -A modality of a method to diagnose the state of the field for today and the future is illustrated by step 210 in Figure

2.

In step 108, process 100 advises optimization for short, medium, and long terms, if optimization is desired.

A modality of a method to determine optimization

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Figure 2.

If optimization is desired, then the user must also select whether the time frame for optimization will be short term, medium term, or long term.

If short-term optimization is desired, then a modality of a method for short-term optimization is illustrated by steps 302-306 in Figure 3. If medium-term optimization is desired, then a modality of a method for medium-term optimization Term is illustrated by steps 402-406 in Figure 4. If long-term optimization is desired, then one embodiment of a method for long-term optimization is illustrated by steps 502-506 in Figure 5.

Optimization can be provided as an automated advisory for reactive and proactive optimization of sweeping efficiency to achieve key performance objectives - including time horizons (from 1 day to any number of years), reducing water management (as a percentage ), reduce downtime for repair times (as a percentage), reduce surplus oil, and increase the recovery of new wells and re-completions (as a percentage). Optimization can also enable timely decisions with

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Database in real time to provide updated, predictive and provide expert system and optimized advice.

In step 110, process 100 includes what-if scenarios to evaluate and compare different optimization scenarios, which can also be considered as optimization war games. A modality of a method to drive scenarios of what would happen if optimization is illustrated by steps 308-316 in Figure 3 for short-term optimization, the steps

408-416 in Figure 4 for medium-term optimization, and steps 508-516 in Figure 5 for long-term optimization.

At. step 112, process 110 implements optimization. An embodiment of a method for obtaining or searching for the optimization implementation is illustrated by steps 318-326 in Figure 3 for short-term optimization, steps 418-426 in Figure 4 for medium-term optimization, and steps 518 -526 in Figure 5 for long-term optimization.

General Process 100 therefore provides a fully integrated subsurface tank management solution to improve sweeping efficiency and allow warehouse and production personnel

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(probably engineers) collaborate. This can be accomplished while monitoring tank dynamics during production, using surface and in-hole sensors, updating and simulating models of the tank and well. This can provide control strategies for short production optimization and increased recovery using surface shutters, ICDs and smart wells while optimization strategies are implemented in future planning, such as infill drilling to recover surplus oil.

The optimization process 100 may be reactive, simple proactive, or enhanced proactive (proactive plus). Reactive optimization can be characterized as an immediate reaction to current conditions. Reactive optimization can happen in the short term and can be directed to actions such as optimizing shutter adjustments and production / injection rates. Simple proactive optimization can be characterized as an action based on predicted conditions, such as to predict the movement of fluid away from the well, and therefore to optimize subsurface operations when taking measures such as plugging a fit valve inside the well in order to increase the total recovery. Simple proactive optimization also focuses on

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If.D'JS íPJAu optimization of long-term field development planning such as scheduling locations, repairs, their configurations, etc. future from producer / infill drilling injector. Improved proactive optimization, on the other hand, provides timing integration of exploration, drilling, completion and production disciplines while evaluating the appropriate action plan to develop a field to ensure there is sufficient time after the identified optimization options that could affect them. Simple proactive optimization can occur over the medium to long term (such as, but not limited to, three months to 2 years) and includes reactive optimization actions along with short and medium term field development plan updates term. Therefore, integration involves running various deposit depletion scenarios as well as cost / benefit analysis scenarios, in real time, thus helping to plan the best integrated solution across all disciplines of a life cycle. asset development. Improved proactive optimization, for example, could allow the operator to change completion and production planning in real time for better final depletion, while actually drilling and gathering additional information about the deposit. The goals of each

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One of these exemplary levels of optimization is illustrated by Table 1500 in Figure 15.

Therefore, process 100 depends on the appropriate time of tank administration including continuous tank viewing, proactive tank diagnosis and optimization, and predictive tank optimization analysis.

Referring now to Figure 2, a flowchart illustrates an embodiment of a method 200 for implementing the present invention.

In step 201, the current condition data or the previously calculated stage condition data is selected using the customer interface and / or the video interface described with reference to Figure

6. Selection of whether to use current condition data or pre-calculated scenario condition data can be based on a subjective determination of whether to use current conditions or previous optimizations. Current condition data provides the ability to assess the current state of the field and to perform optimizations based on that data. Pre-calculated scenario condition data provides the ability to review the past state of the field in relation to the current state and to perform

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i optimizations based on guar data include optimized data for the short, medium or even long term.

In step 202 the current sweep efficiency status is shown using techniques well known in the art and the video interface described with reference to Figure 6. Subsurface display techniques and current sweep efficiency status indicators, for example, can be used with integrated current condition data, pre-calculated scenario condition data, and historical data to provide a visualization of zones, wells , patterns / sectors and / or classified fields representing the current sweep efficiency status. Effective subsurface visualization requires visualization of the reservoir dynamics as the subsurface changes in the well, near the well, and away from the well. A goal of subsurface visualization is to create a very high resolution three-dimensional (3D) visualization interface, which may include different features including fiber optic monitoring visualization, surface deformation visualization, fluid displacement 3D visualization, visualization

Oil surpassed 3D, oil / water interface display, streamline display, field / zone / well maps, isobaric maps,

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injection patterns at subsurface zone levels, and production / injection zone level designations.

In step 204, a future date is selected for the prediction of the sweep efficiency state without optimization and the number of intervention periods is selected using the customer interface and / or the video interface described with reference to Figure 6. The selection of the future date and the periods of intervention is subjective and based on the preference and / or experience of the user. An example of a selected future date for the sweep efficiency state prediction without optimization and the number of intervention periods is illustrated by the graphical user interface 700 in Figure

7, illustrating a future date four (4) years into the future and one-year intervention periods.

At step 206, displays of the predicted sweep efficiency status are generated at the selected future date and at the end of each of the intervention periods using techniques well known in the art and the video interface described with reference to Figure 6. The displays include a sweep efficiency status classification for the identified zones, wells, patterns / sectors, and / or fields as well as others.

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potential user-defined spatial scales. An example of a display of the predicted sweep efficiency status at a selected future date and at the end of each intervention period selected by Figure 7 is illustrated by the graphical user interface 800 in the

Figure 8.

In step 208, one of the predicted sweep efficiency status displays or the current sweep efficiency status display is selected using the customer interface and / or the video interface that is described with reference to Figure 6. Each selected visualization can provide additional detail, including history for sweep efficiency status indicators at any zone, well, pattern / sector and / or field scale.

At step 210, the cause of any undesirable sweep efficiency status indicator for the selected sweep efficiency status display is diagnosed using well known diagnostic techniques, such as those found in DecisionSpace ™ software for simulation of deposits. The cause can be shown by an automated advisory feature that uses gauges including volumetric efficiency, void replacement, jyr PT efficiency

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displacement, nominal pressure and capture factor_:lde, ^ pp: zig (¿/ (Fcap) layer by layer in the reservoir. T,, ~ i— on can be diagnosed by comparing current condition data with historical data or previously calculated scenario condition data Different diagnoses can also be carried out by evaluating a flow or production index that is normalized by a length of the perforation interval. A numerical streamline calculation can also be used to estimate correlation factors and well designation factors.

In step 212, method 200 determines whether production optimization analysis is desired based on the results of step 210. If optimization analysis is desired, then method 200 proceeds to step 214.

Alternatively, method 200 can proceed to steps

218 or 222 if optimization analysis is desired. Optimization analysis may be desirable, for example, if the cause of any undesirable sweep efficiency status indicator is identified by the diagnosis performed in step 210. Otherwise, optimization analysis may not be desirable if there are no undesirable sweep efficiency status indicators. If optimization analysis is not desirable, then method 200 ends.

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At step 214, method 200 determines whether ^lr'I know^r"'' Des&gt;"^!“^r-^{:: i} short-term optimization analysis based on eh 1US “results from step 210 and whether the cause of any undesirable sweep efficiency status indicator can be solved (eg, by adjusting a shutter). If short-term optimization analysis is not desired, then the method

200 proceed to step 218. Alternatively, method 200 may proceed to step 222 if short-term optimization analysis is not desired. If short-term optimization analysis is desired, then method 200 proceeds to step 216.

In step 216, the short-term optimization is carried out. One embodiment of a method for carrying out short-term optimization is illustrated in Figure 3.

In step 218, method 200 determines whether the medium-term optimization analysis based on the results of step 210 is desirable and whether the cause of any undesirable sweep efficiency status indicator cannot be resolved immediately within a matter from one day to a few months (eg equipment repair). If mid-term optimization analysis is not desired, then method 200 proceeds to step 222. Alternatively, method 200 can proceed to step 214 if medium-term optimization analysis is not desired. If you want the

Τ 'medium-term optimization analysis, then “Ítíéto 200 proceeds to step 220.

In step 220, the medium-term optimization is carried out. An embodiment of a method to carry out optimization in the medium term is illustrated in Figure 4.

In step 222, method 200 determines whether long-term optimization analysis based on the results of step 210 is desirable and whether the cause of any undesirable sweep efficiency status indicator cannot be resolved immediately or in a few months but can be resolved within a year or more (eg drilling new wells). The decision between performing short-term optimization analysis, medium-term optimization analysis, or long-term optimization analysis is subjectively based on the experiences and skills of the decision maker. If long-term optimization analysis is not desired, then method 200 ends.

Alternatively, method 200 can proceed to step 214 or step 218 if long-term optimization analysis is not desired. If long-term optimization analysis is desired, then method 200 proceeds to step 224.

In step 224, the long-term optimization is carried out. One embodiment of a method for carrying out long-term optimization is illustrated in Figure 5.

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Referring now to Figure 3, a flow ciiáij'fáma illustrates one embodiment of a method 300 lievar ~ aTcaBo ^ T step 216 in Figure 2.

In step 302, all zones, wells, patterns / groups 5 and / or fields to be optimized are selected from the selected sweep efficiency status display using the customer interface and / or the video interface described with reference to Figure 6.

In step 304, a series of classified optimization scenarios and corresponding actions for reactive optimization are shown using the video interface described with reference to Figure 6 and techniques well known in the art. The series of classified optimization scenarios and corresponding actions for reactive optimization are based on the optimization of the selected zones, wells, patterns, groups and / or fields, which can be exported to a net present value calculator. Thousands of optimization scenarios can be created by simulating the warehouse or using proxy models.

In step 306, one or more optimization scenarios can be selected and the corresponding action for optimization of the selected zones, wells, patterns / groups and / or fields is displayed using the client interface and / or the video interface that is described with

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referring to Figure 6. An example of a corresponding action display is illustrated by the graphical user interface 900 in Figure 9.

In step 310, a prediction date can be selected for each selected optimization scenario using the customer interface and / or video interface described with reference to Figure 6. The prediction date determines the period of time that run each respective selected optimization scenario in a simulator.

In step 312, one or more selected optimization scenarios and the effect of each corresponding action on the selected zones, wells, patterns / groups and / or fields on the prediction date are shown using the video interface described with reference to figure

6. The display may include, for example, changes in sweep efficiency status indicators, different subsurface display parameters for the selected zones, wells, patterns / groups and / or fields, and different derivatives of the net present value for each scenario of selected optimization.

At step 314, method 300 determines whether optimization is desired based on the results of step 312. If optimization is desired, then method 300 proceeds to step

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^: i M i- 'INSTiTUTO.- / L&gt; £ LA C í, ^. O' ·· '· ^ / ·' 316. If optimization is not desired, then the '^l'^,rftétod6^v*^9^-^:!1'’^/proceed to step 318. ~ ~^m ““ “——In step 316, the desired optimization scenario (s) can be selected from said one or more optimization scenarios selected for implementation using the customer interface and / or the video described with reference to Figure

6.

In step 318, the data underlying the results of step 312 is saved.

In step 320, the data underlying the results of step 312 selected in step 316 are saved for implementation.

In step 322, method 300 determines whether the user has action approval to unilaterally implement the desired optimization scenario (s). If the user does not have action approval, then method 300 proceeds to step 324. If the user has action approval, then method 300 proceeds to step 326.

In step 324, a request for implementation of the desired optimization scenario (s) can be generated and / or sent with a business case report, recommendation and analysis using the customer interface and / or the video that is described with reference to insí:

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Figure 6. An example of a request to implement the desired optimization scenario (s) is illustrated by the graphical user interface 1000 in Figure 10.

In step 326, the corresponding action (s) for each optimization scenario to be implemented can be remotely executed or approved for manual implementation using the client interface and / or the video interface that is described with reference to the

Figure 6.

Referring now to Figure 4, a flow chart illustrates an embodiment of a method 400 for carrying out step 220 of Figure 2.

In step 402, all zones, wells, patterns / groups and / or fields to be optimized are selected from the selected sweep efficiency status display using the customer interface and / or the video interface described with reference to Figure 6.

In step 404, a series of classified optimization scenarios and corresponding actions for proactive optimization using the video interface described with reference to Figure 6 and techniques well known in the art are shown. The series of classified optimization scenarios and corresponding actions for proactive optimization are based on the optimization of the zones, wells, patterns, groups and / or fields sélleáéi.anádbiír'yaftle can be exported to a -wwUÍM..pffflganta - noi-n

Optimization actions could be actions such as repairs / network-completions, compliance, surface instrumentation, and others.

In step 406, one or more optimization scenarios can be selected and the corresponding action for optimization of the selected zones, wells, patterns / groups and / or fields is displayed using the client interface and / or the video interface that it is described with reference to Figure 6. An example of selecting one or more optimization scenarios is illustrated by the graphical user interface 1100 in Figure 11.

In step 410, a prediction date can be selected for each selected optimization scenario using the customer interface and / or the video interface described with reference to Figure 6. The prediction date determines the period of time that each respective selected optimization scenario is run in a simulator.

In step 412, said one or more selected optimization scenarios, the effect of each corresponding action on the selected zones, wells, patterns / groups and / or fields on the prediction date, and a plan of

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updated field development for the field with “the¹ cal-éuítZ '* de ¹ respective net present value

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Projected output is shown using the video interface described with reference to Figure 6. The display may include, for example, changes in sweep efficiency status indicators, different subsurface display parameters for zones, wells, patterns / selected groups and / or fields, and different net present value derivatives for each selected optimization scenario. An example of such a display is illustrated by the graphical user interface 1200 and 1300 in the

Figures 12 and 13, respectively.

In step 414, method 400 determines whether optimization is desired based on the results of step 412. If optimization is desired, then method 400 proceeds to step

416. If optimization is not desired, then method 400 proceeds to step 418.

In step 416, you can select the desired optimization scenario (s) from said one or more selected optimization scenarios for implementation using the client interface and / or the video interface described with reference to Figure 6.

In step 418, the data underlying the results of step 412 is saved.

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In step 420, the data is saved in cents .._ a ·, · l-QS Results from step 412 selected in, gl passed Bam ^ aü.

implementation.

In step 422, method 400 determines whether the user has action approval to unilaterally implement the desired optimization scenario (s). If the user does not have action approval, then method 400 proceeds to step 424. If the user has action approval, then method 400 proceeds to step 426. An example of action approval to implement the desired optimization scenario (s) is illustrated by the graphical user interface 1400 in Figure 14.

At step 424, a request for implementation of the desired optimization scenario (s) can be generated and / or sent with a business case report, recommendation and analysis using the customer interface and / or the video that is described with reference to the

Figure 6.

In step 426, the corresponding action (s) for each optimization scenario to be implemented can be remotely executed or approved for manual implementation using the client interface and / or the video interface that is described with reference to the

Figure 6.

Referring now to Figure 5, a flow chart illustrates an embodiment of a method 500 for performing step 224 of Figure 2.

In step 502, all zones, wells, patterns / groups and / or fields to be optimized are selected from the selected sweep efficiency status display using the customer interface and / or the video interface described with reference to Figure 6.

In step 504, a series of classified optimization scenarios and corresponding actions are shown that are derived from the integration disciplines of exploration, drilling, completion, and production of the right moment (the point in the desired future time - short, medium or long term) for enhanced proactive optimization (proactive plus) using the video interface described with reference to Figure 6 and techniques well known in the art while evaluating the appropriate action plan to develop a field. The series of classified optimization scenarios and corresponding actions for proactive plus optimization are based on the optimization of the selected zones, wells, patterns, groups and / or fields, which can be exported to a net present value calculator.

In step 506, one or more optimization scenarios can be selected and the corresponding action for optimization of the selected zones, wells, patterns / groups and / or fields is shown using the client interface and / or the video interface that it is described with reference to Figure 6.

In step 510, a prediction date can be selected for each selected optimization scenario using the client interface and / or the video interface described with reference to Figure 6. The prediction date determines the time period. each respective selected optimization scenario is run in a simulator.

At step 512, said one or more selected optimization scenarios, the effect of each corresponding action on the selected zones, wells, patterns / groups and / or fields on the prediction date, and an updated field development plan for the field with respective net present value calculation and projected production parameters are displayed using the video interface described with reference to Figure 6. The display may include, for example, changes in sweep efficiency status indicators, different subsurface display parameters for the zones,

I selected pits, patterns / groups and / or fields

<img file="MX337098B_D0031.tif" />

and different net present value derivatives for each selected optimization scenario. Optimization scenarios could include actions such as long-term exploration strategies for secondary and tertiary oil recovery, infill drilling, re-drilling of water injection positions, and other field development actions.

In step 514, method 500 determines whether optimization is desired based on the results of step 512. If optimization is desired, then method 500 proceeds to step

516. If optimization is not desired, then method 500 proceeds to step 518.

In step 516, the desired optimization scenario (s) can be selected from said one or more selected optimization scenarios for implementation using the client interface and / or the video interface described with reference to figure

6.

In step 518, the data underlying the results of step 512 is saved.

In step 520, the data underlying the results of step 512 selected in step 516 are saved for implementation.

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In step 522, method 500 determines whether the user has action approval to unilaterally implement the desired optimization scenario (s). If the user does not have action approval, then method 500 proceeds to step 524. If the user has action approval, then method 500 proceeds to step 526.

At step 524, a request for implementation of the desired optimization scenario (s) can be generated and / or sent with a business case report, recommendation and analysis using the customer interface and / or the video that is described with reference to the

Figure 6.

In step 526, the corresponding action (s) for each optimization scenario to be implemented can be remotely executed or approved for manual implementation using the client interface and / or the video interface that is described with reference to the

Figure 6.

System Description

The present invention can be implemented by means of a computer-executable instruction program, such as program models, generally referred to as software applications or executed application programs.

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by a computer. The software may include, pCH?^iliChoose routines, programs, objects, components, '^' 'issLT.LiiJ'lLiidS of data that perform particular tasks or implement particular abstract data types. The software forms the interface to allow a computer to react according to an input source. DecisionSpace ™, which is a commercial software application marketed by Landmark

Graphics Corporation, can be used as an interface application to implement the present invention. The software may also cooperate with other code segments to initiate a variety of tasks in response to the received data in conjunction with the source of the received data.

Other code segments can provide optimization components including, but not limited to, neural networks, earth modeling, historical fit, optimization, visualization, data management, repository simulation, and economics. The software can be stored and / or carried in any variety of memory such as CD-ROM, magnetic disk, bubble memory and semiconductor memory (eg different types of RAM or ROM). Furthermore, the software and its results can be transmitted through a variety of carrier media such as fiber optics, metal wire, and / or through any of a variety of networks, such as the Internet.

On the other hand, those

<img file="MX337098B_D0034.tif" />

Those skilled in the art will appreciate that the invention can be practiced with a variety of computer system configurations, including handheld devices, multiprocessor systems, microprocessor-based or consumer programmable electronics, minicomputers, mainframe computers, and the like. Any number of computer systems and computer networks are acceptable for use with the present invention. The invention can be practiced in distributed computing environments where tasks are carried out by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located on local and remote computer storage media including memory storage devices. The present invention can therefore be implemented in conjunction with different hardware, software, or a combination thereof, in a computer system or other processing system.

Referring to Figure 6, a block diagram illustrates one embodiment of a system for implementing the present invention in a computer. The system includes a computing unit, sometimes referred to as a computing system, that contains memory,

ΙΝ5ΎΓ ·. ' 'i υΞ the application, a client interface, an intertaz'¹'cte vnfeo) and a processing unit. The computation unit is only an example of a suitable computing environment and *

It is not intended to suggest any limitation as to the scope of use or functionality of the invention.

Memory primarily stores application programs, which can also be described as program modules containing computer-executable instructions, executed by the computing unit to implement the present invention described in this document and illustrated in Figure 2. The memory therefore includes a subsurface oil recovery optimization module, which enables the methods illustrated and described with reference to Figure 2 and integrates functionality of the remaining application programs illustrated in Figure 6 . The subsurface oil recovery optimization model, for example, can be used, unlike the current art, to perform many of the functions described with reference to steps 201, 202, 204, 206 (in terms of visualization ), 208, 210, 212, 214, 218, 222, 302, 304 (in terms of display and sorting), 306 (user selection), 308 (in terms of selection), 310, 312 (in terms of display), 314, 316, 318, 320, 322, 324, 326, 402, 404 and ¡i (in terms of display and classification), 406 '7 user selection), 408 (in terms of selection), 410, 412 (in terms of display), 414, 416, 418, 420, 422, 424, 426, 502, 504 (for viewing and sorting), 506 (for user selection), 508 (for viewing), 510, 512 (for viewing), and 514, 516, 518, 520, 522, 524, 526 in Figures 2, 3, 4 and 5. The memory also includes

DecisionSpace ™, which can be used, for example, as an interface application to execute the functions described with reference to steps 206 (regarding the predicted sweep efficiency status), 304 (regarding the calculation of classified scenarios ), 306 (suggested actions), 308 (in terms of the effects), 312 (in terms of the predicted changes in the status indicators of sweeping efficiency), 404 (in terms of the calculation of classified scenarios), 406 (suggested actions), 408 (regarding the effects), 412 (regarding the predicted changes in the sweeping efficiency status indicators), 504 (regarding the calculation of classified scenarios), 506 (suggested actions), 508 (in terms of the effects), and 512 (in terms of the predicted changes in the status indicators of sweeping efficiency) in Figures 2, 3, 4 and 5. Although

DecisionSpace ™ can be used as an interface application, other interface applications can be used,

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4,·. d,. . z, L.ll-íeía recuperae optimization can be used as a instead of, or independent application subsurface oil module.

Although the computing unit is shown as having generalized memory, the computing unit typically includes a variety of computer-readable media.

By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. The memory of the computing system may include computer storage media in the form of volatile and / or non-volatile memory such as Read Only Memory (ROM) and Random Access Memory (RAM). A basic input / output system (BIOS, Basic Input / Output

System), which contains the basic routines that help transfer information between items within the computing unit, such as during startup, is typically stored in ROM. RAM typically contains data and / or program modules that are immediately accessible and / or currently being operated by the processing unit. By way of example, and not limitation, the computing unit includes an operating system, application programs, other program modules, and program data.

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Components displayed in memory can also include other removable / non-removable, volatile / non-volatile computer media or can be implemented in the computing unit through an application program interface (API, Application

Program Interface) or cloud computing, which may reside in a separate computing unit connected through a computer system or network. For example only, a hard disk drive can read from or write to non-removable, non-volatile magnetic media, a magnetic disk drive can read from or write to a removable, non-volatile magnetic disk, and an optical disc drive can read write to or write to a removable, non-volatile optical disc such as a CD-ROM or other optical media. Other removable / non-removable, volatile / non-volatile computer storage media that can be used in the exemplary operating environment may include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile discs, tape digital video, solid state RAM, solid state ROM, and the like. The drives and their associated computer storage media discussed above provide computer-readable instruction storage, data structures, program modules, and other data for

<img file="MX337098B_D0037.tif" />

the computing unit.

A client can enter commands and information into the computing unit through the client interface, which can be input devices such as a keyboard and pointing device, commonly referred to as a mouse, trackball, or touch sensor. The input devices may include a microphone, joystick, satellite antenna, scanner, voice recognition or gesture recognition, or the like. These and other input devices are often connected to the processing unit via a common system link, but can be connected via other interface and common link structures, such as a parallel port or a universal serial bus (USB). ,

Universal Serial Bus).

A monitor or other type of display device can be connected to the common link of the system by means of an interface, such as a video interface. A Graphical User Interface (GUI) can also be used with the video interface to receive instructions from the customer interface and transmit instructions to the processing unit. In addition to the monitor, computers can also include other peripheral output devices such as speakers and printer, which can be connected through an interface.

<img file="MX337098B_D0038.tif" />

peripheral outlet.

Although many other internal components of the computing unit are not shown, those skilled in the art will appreciate that such components and their interconnection are well known.

While the present invention has been described in relation to currently preferred embodiments, it will be understood by those skilled in the art that it is not intended to limit the invention to those embodiments. It is contemplated, therefore, that different modalities and alternative modifications to the disclosed modalities can be made without departing from the spirit and scope of the invention defined by the appended and equivalent claims thereof.

IMR institutes π. OF [

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