WO2020065374A1 - Integrated reservoir management system - Google Patents

Integrated reservoir management system Download PDF

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Publication number
WO2020065374A1
WO2020065374A1 PCT/IB2018/057405 IB2018057405W WO2020065374A1 WO 2020065374 A1 WO2020065374 A1 WO 2020065374A1 IB 2018057405 W IB2018057405 W IB 2018057405W WO 2020065374 A1 WO2020065374 A1 WO 2020065374A1
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Prior art keywords
reservoir
data
engine
management system
integrated
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PCT/IB2018/057405
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French (fr)
Inventor
Jamal Nasir BAHAMAISH
Mohamed Al Marzouqi
Yasser Saeed AL MAZROUEI
Luigi Saputelli
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Abu Dhabi National Oil Company
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Priority to PCT/IB2018/057405 priority Critical patent/WO2020065374A1/en
Publication of WO2020065374A1 publication Critical patent/WO2020065374A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2200/00—Details of seismic or acoustic prospecting or detecting in general
    • G01V2200/10—Miscellaneous details
    • G01V2200/14—Quality control

Definitions

  • the present invention relates to an integrated reservoir management system (IRM system) for the management and control of reservoirs like oil and gas field in the hydrocarbon industry.
  • IRM system integrated reservoir management system
  • Mature reservoirs like oil and gas fields, often referred to as brownfields, are defined as fields in a state of declining production or reaching the end of their productive lives. Recent studies estimate that hydrocarbon production from mature fields will account for more than one half of the global energy mix for the next 20 years, and probably much longer. It is imperative that industry address important issues related to development of mature fields and continue to develop new technologies that will facilitate those developments.
  • Revitalization of mature fields require to apply measures to improve reservoir performance including, for example, identifying bypassed oil, well-intervention management, production stabilization and enhancement (i.e. by stimulation), artificial lift, enhanced oil recovery (EOR), water control, gas-well-liquid removal, infill drilling, accessibility of unconventional reserves from existing facilities and wellbores, sustained casing pressure and its mitigation.
  • measures must be identified and managed for the respective oil and gas reservoir.
  • recommendation action is generated by having a data-modeling portion
  • the prior art document US 2013/0204922 similarly teaches a system for recovering information from a field and managing the asset.
  • the application speaks of‘agents’ being used to carry out these tasks.
  • the agents are defined as coded structures running on a computer system, which is capable of flexible, autonomous, problem solving action, situated in dynamic, open, uncertain, and typically multi-agent environments.
  • an integrated reservoir management system for the management and control of hydrocarbon reservoirs
  • the system comprising a data quality management engine, capable of checking data from multiple real-time, legacy and static data sources for consistency and for substituting poor quality data, and capable of generating a database for validated data; a data- driven model capable of providing integrated reservoir-wells-facilities prediction in response to the validated data through a calibration engine, and capable of generating forecasts in response to the performance optimization engine; an analytic calculation engine capable of calculating reservoir health indicators (RHI) and storing those reservoir health indicators in the RHI database; a diagnostic engine capable of analyzing reservoir health indicators (RHI) and validated data to discover features and to associate those features to reservoir health, and storing conditions of such reservoir health features in a database for insights; a risk engine, capable of identifying reservoir management risks by analyzing the validated data and the insights, and capturing or updating the identified risks in a risk registry; a performance optimization engine, capable of searching for optimum reservoir performance scenarios by interrogating
  • the integrated reservoir management system provides the analytical platform for calculating and storing reservoir health indicators (RHI), initiating and tracking exceptions, and prescribing actions to get back on track.
  • RHI reservoir health indicators
  • the integrated reservoir management system supports the expectation compliance in achievement of production targets that are sustained as per short-term business plans objectives and long-term field development plans, while ensuring that shareholder guidelines are observed and profit key performance indicators are achieved or exceeded continuously.
  • the analytic calculation engine uses validated data, data derived from data- driven models and forecasts.
  • the validated data preferably includes data of real-time, legacy and static data sources.
  • the analytic calculation engine uses data-driven models, which are self- calibrated in response to the validated data, which preferably comprises sensor and legacy data.
  • the reservoir models improve in accuracy over the time by learning the features in the data that are information rich and relevant to the process.
  • the reservoir health indicators comprise: a) visualizations of subsurface characterization maps; and/or
  • WCT water cut
  • GOR gas-oil ratio
  • the recommender engine is further capable of generating recommended actions based on a comparison of reservoir health indicators against established industry benchmark metrics including reservoir analogue and case data.
  • the recommender engine is further capable of generating a set of opportunities in respect to a reservoir based on a comparison of reservoir health indicators against established industry benchmark metrics.
  • the recommender engine assesses and ranks the opportunities by using established industry benchmark metrics including reservoir analogue and case data and provided business goals.
  • the recommender engine uses at least one automated case management engine capable of retrieval and reuse of past similar cases, which operates by reference to similarity index computation.
  • the past similar cases in the case management engine are previously selected and categorized by a computer-algorithm in response to a combination of physical models, established industry standard benchmark metrics and a set of validated case histories in a case database.
  • the performance optimization engine uses reservoir health indicators and forecast data for generating opportunities of reservoir performance optimization, which are further analyzed by the recommender engine.
  • the integrated reservoir management system further comprises a business process management engine, which is capable of enabling process governance.
  • the integrated reservoir management system provides a business process management platform for automating the system to people and people to system interactions for enabling a smarter governance of data gathering, reports and procedures of current reservoir management process, providing a way for continuously improving reservoir management.
  • the business process management engine is capable of: a. automatically monitoring and controlling workflows for reservoir review by exemption in tight integrations with the visualization dashboards; and/or b. processing, synthesizing and enabling interpretation of large volume data and reservoir data types; and/or
  • the integrated reservoir management system encompasses a broader view of the process than in the prior art, moving from notion of what needs to be done by regulatory procedures to what is happening across all disciplines all the time.
  • the integrated reservoir management system is portable and can be replicated and deployed from one reservoir to another, assisted by the process configurator, while keeping the overall control on the standard process through the business process management engine.
  • the integrated reservoir management system provides a role-based integrated visualization and decision support to all system stakeholders.
  • the integrated visualization and decision support is capable of automatically extracting data stores in all system data sources and presenting those to the right stakeholder; analyzing data by exception, automatically generating standard and ad- hoc reports, and triggering workflows for reservoir review by exemption.
  • the integrated reservoir management system provides process key performance indicators (KPI’s) to allow continuous process improvement monitoring.
  • Fig. l a schematically overview of an embodiment of an integrated reservoir
  • Fig. 2 an embodiment of a reservoir health overview dashboard
  • Fig. 3 an embodiment of a sector health overview dashboard
  • Fig. 4 an embodiment of a reservoir health diagnostic plots dashboard.
  • Fig. 1 shows an overview of an embodiment of an integrated reservoir management system 1, the data sources 100 and the related users 130-137.
  • the integrated reservoir management system 1 is used for the management and control of hydrocarbon reservoirs.
  • Such hydrocarbon reservoirs comprise oil and gas fields, which may be new or already matured.
  • the main components of the integrated reservoir management system 1 are a data quality management engine 110, a data-driven model 113, an analytic calculation engine 114, a diagnostic engine 115, a risk engine 116, a performance optimization engine and a recommender engine 122. Further components are a case management engine 129, a process configurator 139, a calibration engine 112, a business process management engine 138 and a role-based integrated visualization and decision support engine 128. All such components of the integrated reservoir management system 1 can be implemented in software and hardware on commonly available computer systems.
  • the data sources 100 comprise real-time data, captured at the reservoirs directly, legacy data and statistic data sources.
  • Such data may comprise in group 101, the rate, pressure, temperature of the produced product, the status of the well, and well related events, in group 102 open-hole logs and cased-hole logs, in group 103 the volume, uptime and static pressure of an asset, in group 104 well and reservoir data, in group 105 well work job data and performance data, in group 106 seismic and geology data, in group 107 financial and project data and in group 108 analogue and case data.
  • the data sources are retrieved by a data quality management engine 110 which checks the incoming data for consistency and substitutes the poor quality data by better quality data.
  • the calculation engine included in the data quality management engine 110 preferably solves a gross error function between actual and expected values, subject to a set of constraints given by a full-physics-based and a data-driven process model.
  • Validated data of the data quality management engine 110 is stored in the validated data database 111.
  • the validated data is used by the analytic calculation engine 114 for calculating reservoir performance indicators.
  • the analytic calculation engine 114 uses data-driven models 113.
  • the data-driven models 113 are preferably fast- updated reservoir-wells-facilities models which are self-calibrated in response validated sensor and legacy data 101-104.
  • the reservoir performance indicators are stored in a reservoir performance indicator database 118.
  • the reservoir health indicators may comprise visualizations of subsurface
  • a performance optimization engine 121 uses such reservoir health indicators 118 and forecast data 117 for calculating opportunities of reservoir performance optimization. The performance optimization engine 121 generates an ensemble of opportunities based on calculated reservoir performance indicators and multiple scenario, run with the available models.
  • Insight data 119 and risk registry data 120 were used together with the calculated opportunities of reservoir performance optimization by a recommender engine 122.
  • the recommender engine 122 compares the reservoir performance indicators against established industry benchmark metrics and in response thereof it recommends a set of actions with a certain priority ranking.
  • the recommender engine 122 can further calculate a reservoir health diagnostics and a set of opportunities based on a
  • the recommender engine 122 uses at least one automated reasoning case management system, which operates by reference to a set of exemplar cases. Preferably, for generating a recommendation the recommender engine 122 can select from the top-ranked opportunities, can use a combination of case-based rules, benchmark data and bayesian belief networks.
  • the automated case management system of the recommender engine 122 executes a set of self-learning algorithms with a set of technical and business conditional rules for deriving reservoir health insights based on available real-time sensor and legacy data, set of data-driven models, reservoir health performance indicators and key diagnostic plots, recommending a set of actions with certain priority ranking in response to the said diagnosis, recommending a set of opportunities with certain priority ranking in response to the said diagnosis, recommending a set of reservoir surveillance requirements addressing ongoing uncertainties and challenges, initiating a set of decisions through a business process management (BPM) application, and
  • BPM business process management
  • the set of opportunities include well work to make changes to wellbore completion and reservoir productivity (stimulation), review of drilling strategy, review of injection scheme, facilities upgrade, optimize/ change production/injection rates, review of surveillance strategy, etc.
  • integrated reservoir management system l is based on a set of inter-related workflows capable of being automatically executed. Each workflow has a specific function within the diagnosis, opportunity identification process and overall performance management tasks.
  • the automated reasoning systems of the recommender engine 122 operate by reference to a set of exemplar cases, i.e. on a“case base”, to which the facts of a particular situation, i.e. the“problem”, may be matched.
  • the exemplar cases are previously selected and categorized by a computer-algorithm in response to a combination of physical models, established industry standard benchmark metrics and a set of validated case histories.
  • the IRM system provide basis for comparing predicted reservoir performance to actual performance with projections including alerts when periodic business goals are likely to be missed, comparing reservoir performance indicators by exception as mentioned above (including key metrics such as production compliance, recoveiy factors, fluid ratios, well allocation factors, injection efficiency, voidage replacement ratio (VRR) performance, model uncertainty, and rate reallocation), ranking opportunities and rolled up to show full reservoir and field potential, providing awareness of the short-term operating cost per unit barrel.
  • exception including key metrics such as production compliance, recoveiy factors, fluid ratios, well allocation factors, injection efficiency, voidage replacement ratio (VRR) performance, model uncertainty, and rate reallocation
  • the preferred output of the integrated reservoir management system 1 comprises:
  • a set of recommended actions with priority ranking which may include
  • a set of updated reservoir performance enhancement opportunities is visualized by a role-based integrated visualization and decision support engine 128 to the various users of the integrated reservoir management system 1.
  • Such users comprise reservoir engineers 130, petroleum engineers 131, operations engineering 132, geologists and geophysicists 133, petrophysics 134, simulation engineering 135, drilling engineering 136, business planning 137 and others.
  • Fig. 2 shows an exemplary embodiment of a reservoir health overview dashboard 201 generated by the integrated visualization and decision support engine 128.
  • the reservoir health overview dashboard 201 shows reservoir health KPI’s 210.
  • the KPI’s are color coded (green, yellow, red, gray) for easily showing the actual status of the respective reservoir regarding a particular KPI.
  • Window 215 shows a production and injection chart.
  • Window 220 shows charts for pressures and VRR.
  • Window 225 shows a saturation map and the well status.
  • Window 230 shows the number of active and inactive strings in chart.
  • Fig. 3 shows an exemplary embodiment of a sector health overview dashboard 202 generated by the integrated visualization and decision support engine 128.
  • Window 210 like in Fig. 2 shows the reservoir health KPI’s.
  • Window 235 shows measured data and results indicators.
  • Window 240 shows a saturation map and the well status of a sector.
  • Window 245 shows the reservoir health KRG.
  • Window 250 shows rates on selected items.
  • Fig. 4 shows an exemplary embodiment of a reservoir health diagnostic plots dashboard 203.
  • Window 210 shows the reservoir health KPI’s, like in the dashboards of Fig. 2 and Fig. 3.
  • Fig. 255 shows charts of pressures and VRR.
  • Window 260 shows a saturation map and well status.
  • Window 265 shows a diagram of measured pressures.
  • the integrated reservoir management system 1 further comprises a business process management engine 138, that is capable of enabling process governance.
  • the integrated reservoir management system 1 provides the governance to the reservoir management technical and business processes, establishing a collaborative work and information management virtual environment that enhances the execution of reservoir management activities, and by coordinating technical teams working in the context of well-defined business processes, supported by a well nurtured information ecosystem.
  • Preferably business process management engine 138 is capable of automatically controlling workflows for reservoir review by exemption, processing, synthesizing and enabling interpretation of large volume data and reservoir data types, automatically identifying and registering actions for establishing if reservoirs are performing as per previous established plans, automatically identifying and registering actions to optimize business, reservoir and well performance, automatically linking decisions, analysis and insight from multiple disciplines, automatically adding weights to the decisions, analysis and insight from multiple disciplines in response to case accuracy, automatically enabling opportunity assessment and ranking, automatically enabling decision making.
  • the integrated reservoir management system 1 acts as a knowledge capturing system as it preserves the information used (input) or generated (results) during each execution of a workflow so that users can go back in time and inspect the information that was available at the time the work was carried out, and the results derived based on available information.
  • the Case Management Engine 129 is responsible for this. Further, it enables to capture and disseminate knowledge of workflows within the execution of the workflow.
  • the integrated reservoir management system 1 system further captures knowledge form users that can be further used to enhance the quality of the workflow itself, and it can be easily disseminated for the next instance of the workflow execution, helping teams adopt best practices applicable to their tasks.
  • the integrated reservoir management system 1 further provides a collaborative work environment for IRM teams, that enables users to manage their key activities through a simple to use action oriented web interface.
  • the integrated reservoir management system 1 can capture and store process KPI’s 126 to understand workflow performance statistics to learn how resources are utilized, identify bottlenecks, and thereby understand the scope for improving effectiveness.
  • the integrated reservoir management system 1 ensures that business process documentation is completely aligned with other operating procedures.
  • the integrated reservoir management system 1 allows updating dynamically process documentation as workflows are developed and enhanced.
  • Each process owner has real-time and historic access to process execution metrics that are obtained automatically by the integrated reservoir management system 1. There is no need to fill-up performance tracking spreadsheets or running ad-hoc reports.
  • the integrated reservoir management system 1 enables process orchestration through the Business Process Management Engine 138 which is reconfigured automatically by the Case Management Engine 129.
  • Each process user receives automated notification that there are tasks pending for action for his role, triggered because of tasks completed by other roles. As the user works on and completes pending tasks, the system tracks the progress of the overall process and routes notification for actions to those responsible for the next task.
  • the process configurator 130 allows systems administrators to manage the installation of the integrated reservoir management system in multiple sites across the organization or multiple instance within one organization. Management of installation may include the planning of hardware and system requirements, configurations of software and hardware, integration of data sources, stakeholder administration, user role, configurations, security configurations and other system administration procedures. Process configurator 130 dictates the asset type and process available in the Business Process Management Engine 138 which is reconfigured automatically by the Case Management Engine 129.
  • the diagnostic engine 115 integrates the data information available in the RHI database 118 along with validated data 111 to discover features in the data.
  • the diagnostic engine 115 uses fuzzy logic rules system to determine whether certain conditions obtained from the calculated results indicated in the insights database 119 and in the validated database 111 could translate into a relevant condition that could become a precursor event to reservoir performance detriment. Additionally, the diagnostic engine 115 associate those features to reservoir health, and storing those conditions in the insights database 119.
  • the risk engine 116 integrates the data information available in the insights database 119, validated data 111 from historic reservoir and well performance, and in relation to the established business goals 109.
  • the risk engine verifies whether reservoir health indicators will impact the compliance of production targets, sustainability of production, cost compliance, and other goals.
  • the risk engine 116 uses Bayesian inference rules to determine whether certain conditions triggered from the previous results as indicated in the insights database 119 and in the context of reservoir and well conditions 111 are potential reservoir risks.
  • the risk engine 116 assigns severity and potential loss associated to the identified risk.
  • the identified risks are captured and or updated in the risk registry 120.

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Abstract

The present invention relates to an integrated reservoir management system (1) for the management and control of hydrocarbon reservoirs, the system comprising a data quality management engine (110), capable of checking data from multiple real-time, legacy and static data sources (100) for consistency and for substituting poor quality data, and capable of generating a database for validated data (111); a data-driven model (13), capable of providing integrated reservoir-wells-facilities prediction in response to the validated data (111) through a calibration engine (112), and capable of generating forecasts (117) in response to a performance optimization engine (121); an analytic (10) calculation engine (114), capable of calculating reservoir health indicators RHI and storing those reservoir health indicators in the RHI database (118); a diagnostic engine (15), capable of analyzing the reservoir health indicators (118) and the validated data (111) to discover features and to associate those features to reservoir health, and storing conditions of such reservoir health features in a database for insights (119); a risk engine (116), capable of identifying reservoir management risks by analyzing the validated data (11) and the insights (119), and capturing or updating the identified risks in a risk registry (20); a performance optimization engine (121), capable of searching for optimum reservoir performance scenarios by interrogating the data-driven model (113) and providing results scenarios for a recommender engine (122); and a recommender engine (122), capable of assimilating scenarios from the performance optimization engine (121), insights database (119) and risk registry (120), comparing reservoir health indicators against established industry benchmark metrics and in response thereof recommending a set of actions (123), recommending a set of surveillance requirements (124) and recommending a set of ranked opportunities (125).

Description

Integrated Reservoir Management System
1. Field of the invention
The present invention relates to an integrated reservoir management system (IRM system) for the management and control of reservoirs like oil and gas field in the hydrocarbon industry.
2. Prior art
Mature reservoirs, like oil and gas fields, often referred to as brownfields, are defined as fields in a state of declining production or reaching the end of their productive lives. Recent studies estimate that hydrocarbon production from mature fields will account for more than one half of the global energy mix for the next 20 years, and probably much longer. It is imperative that industry address important issues related to development of mature fields and continue to develop new technologies that will facilitate those developments.
Revitalization of mature fields require to apply measures to improve reservoir performance including, for example, identifying bypassed oil, well-intervention management, production stabilization and enhancement (i.e. by stimulation), artificial lift, enhanced oil recovery (EOR), water control, gas-well-liquid removal, infill drilling, accessibility of unconventional reserves from existing facilities and wellbores, sustained casing pressure and its mitigation. Such measures must be identified and managed for the respective oil and gas reservoir.
Traditionally, reservoir management decisions have been supported using several industiy standard tools like diagnostic plots, reservoir characterization, value of information analysis, risk and uncertainty analysis, analogue benchmarking, as well as data-driven, numeric and analytical models and techniques.
Data-driven methods have been used in the past to generate reservoir models that are capable to rapidly generate scenarios. However, an integrated reservoir performance management and business process management, which uses data-driven methods and techniques to be able to represent the overall performance of the porous media system, including the surface production/injection network used by oil, water, and gas across the asset, and the interaction among disciplines, has not been described.
Integrated reservoir management was described in literature already. Most applications have been suggested towards recommending actions on specific reservoir management objectives of a given asset or oil and gas field. The document Popa A., and Casssidy S., “Implementing ί-field integrated solutions for reservoir management: A case study in San Joaquin Valley”, SPE-143950, SPE Economics and Management, January 2012, is an example of such literature.
The prior art document US 9,710,766 teaches a management system to be implemented for a petroleum producing filed, which provides an indicator and metric which is designed to assess methods for improving efficiency of the field. The application teaches further the use of data acquired from global benchmark analysis of similar fields for improving efficiency.
The prior art document US 8,380,642 describes a feedback system, where a
recommendation action is generated by having a data-modeling portion and
knowledge-based portion.
The prior art document US 2013/0204922 similarly teaches a system for recovering information from a field and managing the asset. The application speaks of‘agents’ being used to carry out these tasks. The agents are defined as coded structures running on a computer system, which is capable of flexible, autonomous, problem solving action, situated in dynamic, open, uncertain, and typically multi-agent environments.
The prior art document U.S. 8,577,613 deals with exploration of fields and teaches a management system which involves iterative processing to allow assumptions and probabilistic models to optimize the set of recommended activities currently being performed, the optimization being over time as additional knowledge is gained. However, there is a need for an improved integrated reservoir management system which supports the complete workflow of reservoir management to allow people focusing in the most important things for each specific reservoir type and condition at any point in the reservoir life, thus reducing errors, saving time and providing agility to enhance business performance.
3. Summary of the invention
The above mentioned problems are solved by an integrated reservoir management system according to claim 1.
Particularly, the above mentioned problems are solved an integrated reservoir management system for the management and control of hydrocarbon reservoirs, the system comprising a data quality management engine, capable of checking data from multiple real-time, legacy and static data sources for consistency and for substituting poor quality data, and capable of generating a database for validated data; a data- driven model capable of providing integrated reservoir-wells-facilities prediction in response to the validated data through a calibration engine, and capable of generating forecasts in response to the performance optimization engine; an analytic calculation engine capable of calculating reservoir health indicators (RHI) and storing those reservoir health indicators in the RHI database; a diagnostic engine capable of analyzing reservoir health indicators (RHI) and validated data to discover features and to associate those features to reservoir health, and storing conditions of such reservoir health features in a database for insights; a risk engine, capable of identifying reservoir management risks by analyzing the validated data and the insights, and capturing or updating the identified risks in a risk registry; a performance optimization engine, capable of searching for optimum reservoir performance scenarios by interrogating the data-driven model and providing results scenarios for a recommender enginge; and a recommender engine capable of assimilating scenarios from the performance optimization engine, insights database and risk registiy, comparing reservoir health indicators against established industry benchmark metrics and in response thereof recommending a set of actions, recommending a set of surveillance requirements, and recommending a set of ranked opportunities. The integrated reservoir management system provides the analytical platform for calculating and storing reservoir health indicators (RHI), initiating and tracking exceptions, and prescribing actions to get back on track. The integrated reservoir management system supports the expectation compliance in achievement of production targets that are sustained as per short-term business plans objectives and long-term field development plans, while ensuring that shareholder guidelines are observed and profit key performance indicators are achieved or exceeded continuously.
The following are key advantages of the integrated reservoir management system:
• Shorter reservoir performance review cycles, by over 80%, from years to months.
• Possibility to monitor the quality and quantity of effort invested to validate data, analyze data, perform integration and visualization, generate opportunities and monitor the execution of reservoir management action.
• Enhanced human resource utilization by 300%.
• Enhanced reservoir activity predictability.
• Reduced operating costs by smart adoption of key engineering workflows to
maximize efficiency.
• Possibility to develop a better understanding of reservoir dynamics, identifying factors that control sweep efficiency and water movement, enhancing model predictive capabilities and exploring ways for production enhancement and improving oil recovery via optimum rate control.
• Faster decision making by using reservoir software available in existing
resources.
• Possibility, to ensure production sustainability and mitigate shortfalls
proactively.
Other indirect advantages of the integrated reservoir management system are to enable integrated reservoir management framework practices in order to:
• Optimize integrated reservoir management workflow automation
implementation with maximum economic efficiency;
• Continuously enhance the quality of the system;
• Shorten implementation time in new assets; and
• Reduce operating costs related to implementation in terms of effort for humans, hardware and software. Preferably, the analytic calculation engine uses validated data, data derived from data- driven models and forecasts. Thus, poor quality data is excluded from further processing and it is replaced by better quality approximations whenever available. The validated data preferably includes data of real-time, legacy and static data sources.
Preferably, the analytic calculation engine uses data-driven models, which are self- calibrated in response to the validated data, which preferably comprises sensor and legacy data. Thus, the reservoir models improve in accuracy over the time by learning the features in the data that are information rich and relevant to the process.
Preferably, the reservoir health indicators comprise: a) visualizations of subsurface characterization maps; and/or
b) 2D and / or 3D scatter cross plots; and/ or
c) historic and/or simulated production/injection rates/volumes; and/or d) reservoir performance diagnostic plots; and/ or
e) pressure and saturation maps; and/or
f) production injection streamlines; and/or
g) production/injection rates and pressure trends; and/or
h) recovery factors; and/ or
i) water or gas flood sweep efficiency; and/ or
j) fluid ratios, including water cut (WCT) and gas-oil ratio (GOR); and/or k) well and reservoir allocation factors; and/ or
l) injection efficiency; and/or
m) voidage replacement ratio (VRR) performance; and/or
n) reservoir model uncertainty; and/ or
0) allocated production volume; and/ or
p) well and facility availability; and/ or
q) reservoir activity costs; and/ or
r) reservoir activity predictability Preferably, the recommender engine is further capable of generating recommended actions based on a comparison of reservoir health indicators against established industry benchmark metrics including reservoir analogue and case data.
Preferably, the recommender engine, is further capable of generating a set of opportunities in respect to a reservoir based on a comparison of reservoir health indicators against established industry benchmark metrics.
Preferably, the recommender engine, assesses and ranks the opportunities by using established industry benchmark metrics including reservoir analogue and case data and provided business goals.
Preferably, the recommender engine uses at least one automated case management engine capable of retrieval and reuse of past similar cases, which operates by reference to similarity index computation.
Preferably, the past similar cases in the case management engine are previously selected and categorized by a computer-algorithm in response to a combination of physical models, established industry standard benchmark metrics and a set of validated case histories in a case database.
Preferably, the performance optimization engine uses reservoir health indicators and forecast data for generating opportunities of reservoir performance optimization, which are further analyzed by the recommender engine.
Preferably, the integrated reservoir management system further comprises a business process management engine, which is capable of enabling process governance. By the business process management engine, the integrated reservoir management system provides a business process management platform for automating the system to people and people to system interactions for enabling a smarter governance of data gathering, reports and procedures of current reservoir management process, providing a way for continuously improving reservoir management.
Preferably, the business process management engine is capable of: a. automatically monitoring and controlling workflows for reservoir review by exemption in tight integrations with the visualization dashboards; and/or b. processing, synthesizing and enabling interpretation of large volume data and reservoir data types; and/or
c. automatically identifying, registering, initiating and tracking actions for establishing if reservoirs are performing as per previous established plan; and/or
d. automatically identifying, registering, initiating and tracking actions to
optimize business, reservoir and well performance; and/ or
e. automatically linking decisions, analysis and insights from multiple
disciplines; and/or
f. automatically adding weights to the decisions, analysis and insights from multiple disciplines in response to case accuracy; and/ or
g. automatically enabling opportunity assessment and ranking; and/or h. automatically enabling decision making.
By such tasks the integrated reservoir management system encompasses a broader view of the process than in the prior art, moving from notion of what needs to be done by regulatory procedures to what is happening across all disciplines all the time.
Preferably, the integrated reservoir management system is portable and can be replicated and deployed from one reservoir to another, assisted by the process configurator, while keeping the overall control on the standard process through the business process management engine.
Preferably, the integrated reservoir management system provides a role-based integrated visualization and decision support to all system stakeholders.
Preferably, the integrated visualization and decision support is capable of automatically extracting data stores in all system data sources and presenting those to the right stakeholder; analyzing data by exception, automatically generating standard and ad- hoc reports, and triggering workflows for reservoir review by exemption. Preferably, the integrated reservoir management system provides process key performance indicators (KPI’s) to allow continuous process improvement monitoring.
4. Brief description of the drawings
In the following preferred embodiments of the invention are described with respect to the figures, in which shows:
Fig. l: a schematically overview of an embodiment of an integrated reservoir
management system, the data sources and of the users;
Fig. 2: an embodiment of a reservoir health overview dashboard;
Fig. 3: an embodiment of a sector health overview dashboard; and
Fig. 4: an embodiment of a reservoir health diagnostic plots dashboard.
5. Description of preferred embodiments
In the following preferred embodiments of the invention are described with respect to the figures.
Fig. 1 shows an overview of an embodiment of an integrated reservoir management system 1, the data sources 100 and the related users 130-137. The integrated reservoir management system 1 is used for the management and control of hydrocarbon reservoirs. Such hydrocarbon reservoirs comprise oil and gas fields, which may be new or already matured.
The main components of the integrated reservoir management system 1 are a data quality management engine 110, a data-driven model 113, an analytic calculation engine 114, a diagnostic engine 115, a risk engine 116, a performance optimization engine and a recommender engine 122. Further components are a case management engine 129, a process configurator 139, a calibration engine 112, a business process management engine 138 and a role-based integrated visualization and decision support engine 128. All such components of the integrated reservoir management system 1 can be implemented in software and hardware on commonly available computer systems. The data sources 100 comprise real-time data, captured at the reservoirs directly, legacy data and statistic data sources. Such data may comprise in group 101, the rate, pressure, temperature of the produced product, the status of the well, and well related events, in group 102 open-hole logs and cased-hole logs, in group 103 the volume, uptime and static pressure of an asset, in group 104 well and reservoir data, in group 105 well work job data and performance data, in group 106 seismic and geology data, in group 107 financial and project data and in group 108 analogue and case data.
The data sources are retrieved by a data quality management engine 110 which checks the incoming data for consistency and substitutes the poor quality data by better quality data. The calculation engine included in the data quality management engine 110 preferably solves a gross error function between actual and expected values, subject to a set of constraints given by a full-physics-based and a data-driven process model. Validated data of the data quality management engine 110 is stored in the validated data database 111.
The validated data is used by the analytic calculation engine 114 for calculating reservoir performance indicators. For such calculations, the analytic calculation engine 114 uses data-driven models 113. The data-driven models 113 are preferably fast- updated reservoir-wells-facilities models which are self-calibrated in response validated sensor and legacy data 101-104. The reservoir performance indicators are stored in a reservoir performance indicator database 118.
The reservoir health indicators may comprise visualizations of subsurface
characterization maps, 2D and/or 3D scatter cross plots, historic and/or simulated production rates, historic and/or simulated injection rates, historic and/or simulated volumes times series, reservoir performance diagnostic plots, pressure and saturation maps, production injection streamlines, production and injection trends, recovery factors, fluid ratios, well allocation factors, injection efficiency, voidage replacement ratio (VRR) performance, model uncertainty, allocated production volume, well and facility availability; reservoir activity costs, reservoir activity predictability a and other indicators. A performance optimization engine 121 uses such reservoir health indicators 118 and forecast data 117 for calculating opportunities of reservoir performance optimization. The performance optimization engine 121 generates an ensemble of opportunities based on calculated reservoir performance indicators and multiple scenario, run with the available models.
Insight data 119 and risk registry data 120 were used together with the calculated opportunities of reservoir performance optimization by a recommender engine 122.
The recommender engine 122 compares the reservoir performance indicators against established industry benchmark metrics and in response thereof it recommends a set of actions with a certain priority ranking. The recommender engine 122 can further calculate a reservoir health diagnostics and a set of opportunities based on a
comparison of reservoir performance indicators against established industry benchmark metrics. The recommender engine 122 uses at least one automated reasoning case management system, which operates by reference to a set of exemplar cases. Preferably, for generating a recommendation the recommender engine 122 can select from the top-ranked opportunities, can use a combination of case-based rules, benchmark data and bayesian belief networks.
The automated case management system of the recommender engine 122 executes a set of self-learning algorithms with a set of technical and business conditional rules for deriving reservoir health insights based on available real-time sensor and legacy data, set of data-driven models, reservoir health performance indicators and key diagnostic plots, recommending a set of actions with certain priority ranking in response to the said diagnosis, recommending a set of opportunities with certain priority ranking in response to the said diagnosis, recommending a set of reservoir surveillance requirements addressing ongoing uncertainties and challenges, initiating a set of decisions through a business process management (BPM) application, and
continuously tracking such decisions through a computer-based reporting system, continuously tracking such ongoing uncertainties and challenges over time,
continuously tracking such execution of actions and closure rate over time continuously tracking such execution of opportunities and their success rate over time. The set of opportunities include well work to make changes to wellbore completion and reservoir productivity (stimulation), review of drilling strategy, review of injection scheme, facilities upgrade, optimize/ change production/injection rates, review of surveillance strategy, etc.
In addition, integrated reservoir management system l is based on a set of inter-related workflows capable of being automatically executed. Each workflow has a specific function within the diagnosis, opportunity identification process and overall performance management tasks.
The automated reasoning systems of the recommender engine 122 operate by reference to a set of exemplar cases, i.e. on a“case base”, to which the facts of a particular situation, i.e. the“problem”, may be matched. The exemplar cases are previously selected and categorized by a computer-algorithm in response to a combination of physical models, established industry standard benchmark metrics and a set of validated case histories.
Based on a set of established exemplar cases, the IRM system provide basis for comparing predicted reservoir performance to actual performance with projections including alerts when periodic business goals are likely to be missed, comparing reservoir performance indicators by exception as mentioned above (including key metrics such as production compliance, recoveiy factors, fluid ratios, well allocation factors, injection efficiency, voidage replacement ratio (VRR) performance, model uncertainty, and rate reallocation), ranking opportunities and rolled up to show full reservoir and field potential, providing awareness of the short-term operating cost per unit barrel.
The preferred output of the integrated reservoir management system 1 comprises:
0 A diagnosis in regard to reservoir health.
0 A set of recommended actions with priority ranking which may include
reservoir management guidelines, production and injection rates per reservoir and wells and inputs for the following 5-years business plans
0 A set of reservoir surveillance requirements.
0 A set of updated reservoir performance enhancement opportunities. Such output is visualized by a role-based integrated visualization and decision support engine 128 to the various users of the integrated reservoir management system 1. Such users comprise reservoir engineers 130, petroleum engineers 131, operations engineering 132, geologists and geophysicists 133, petrophysics 134, simulation engineering 135, drilling engineering 136, business planning 137 and others.
Fig. 2 shows an exemplary embodiment of a reservoir health overview dashboard 201 generated by the integrated visualization and decision support engine 128. The reservoir health overview dashboard 201 shows reservoir health KPI’s 210. The KPI’s are color coded (green, yellow, red, gray) for easily showing the actual status of the respective reservoir regarding a particular KPI. Window 215 shows a production and injection chart. Window 220 shows charts for pressures and VRR. Window 225 shows a saturation map and the well status. Window 230 shows the number of active and inactive strings in chart.
Fig. 3 shows an exemplary embodiment of a sector health overview dashboard 202 generated by the integrated visualization and decision support engine 128. Window 210 like in Fig. 2 shows the reservoir health KPI’s. Window 235 shows measured data and results indicators. Window 240 shows a saturation map and the well status of a sector. Window 245 shows the reservoir health KRG. Window 250 shows rates on selected items.
Fig. 4 shows an exemplary embodiment of a reservoir health diagnostic plots dashboard 203. Window 210 shows the reservoir health KPI’s, like in the dashboards of Fig. 2 and Fig. 3. Fig. 255 shows charts of pressures and VRR. Window 260 shows a saturation map and well status. Window 265 shows a diagram of measured pressures.
The integrated reservoir management system 1 further comprises a business process management engine 138, that is capable of enabling process governance. The integrated reservoir management system 1 provides the governance to the reservoir management technical and business processes, establishing a collaborative work and information management virtual environment that enhances the execution of reservoir management activities, and by coordinating technical teams working in the context of well-defined business processes, supported by a well nurtured information ecosystem.
Preferably business process management engine 138 is capable of automatically controlling workflows for reservoir review by exemption, processing, synthesizing and enabling interpretation of large volume data and reservoir data types, automatically identifying and registering actions for establishing if reservoirs are performing as per previous established plans, automatically identifying and registering actions to optimize business, reservoir and well performance, automatically linking decisions, analysis and insight from multiple disciplines, automatically adding weights to the decisions, analysis and insight from multiple disciplines in response to case accuracy, automatically enabling opportunity assessment and ranking, automatically enabling decision making.
Further, the integrated reservoir management system 1 acts as a knowledge capturing system as it preserves the information used (input) or generated (results) during each execution of a workflow so that users can go back in time and inspect the information that was available at the time the work was carried out, and the results derived based on available information. The Case Management Engine 129 is responsible for this. Further, it enables to capture and disseminate knowledge of workflows within the execution of the workflow. The integrated reservoir management system 1 system further captures knowledge form users that can be further used to enhance the quality of the workflow itself, and it can be easily disseminated for the next instance of the workflow execution, helping teams adopt best practices applicable to their tasks. The integrated reservoir management system 1 further provides a collaborative work environment for IRM teams, that enables users to manage their key activities through a simple to use action oriented web interface. The integrated reservoir management system 1 can capture and store process KPI’s 126 to understand workflow performance statistics to learn how resources are utilized, identify bottlenecks, and thereby understand the scope for improving effectiveness. The integrated reservoir
management system 1 ensures that business process documentation is completely aligned with other operating procedures. The integrated reservoir management system 1 allows updating dynamically process documentation as workflows are developed and enhanced. Each process owner has real-time and historic access to process execution metrics that are obtained automatically by the integrated reservoir management system 1. There is no need to fill-up performance tracking spreadsheets or running ad-hoc reports. The integrated reservoir management system 1 enables process orchestration through the Business Process Management Engine 138 which is reconfigured automatically by the Case Management Engine 129. Each process user receives automated notification that there are tasks pending for action for his role, triggered because of tasks completed by other roles. As the user works on and completes pending tasks, the system tracks the progress of the overall process and routes notification for actions to those responsible for the next task. Each time a user initiates a task within a process, there will be easy access to on-line guidelines and standards to help performing such task as specified by the process owner. Changes on standard operating procedures could be immediately made available to those executing tasks within the integrated reservoir management workflows. As the automation maturity level advances for integrated reservoir management system 1, there will be more processes that can automatically get the required technical data from the project and master databases ready to be used for the intended purpose. Similarly, information generated or validated during the process could be pushed back into the project repository to be made available to subsequent processes. Data rules and IRM business rules (defined through the IRM governance) are put into the system in the context of each workflow. Those will be enriched and optimized as the process and standards become more mature.
The process configurator 130 allows systems administrators to manage the installation of the integrated reservoir management system in multiple sites across the organization or multiple instance within one organization. Management of installation may include the planning of hardware and system requirements, configurations of software and hardware, integration of data sources, stakeholder administration, user role, configurations, security configurations and other system administration procedures. Process configurator 130 dictates the asset type and process available in the Business Process Management Engine 138 which is reconfigured automatically by the Case Management Engine 129.
The diagnostic engine 115 integrates the data information available in the RHI database 118 along with validated data 111 to discover features in the data. The diagnostic engine 115 uses fuzzy logic rules system to determine whether certain conditions obtained from the calculated results indicated in the insights database 119 and in the validated database 111 could translate into a relevant condition that could become a precursor event to reservoir performance detriment. Additionally, the diagnostic engine 115 associate those features to reservoir health, and storing those conditions in the insights database 119.
The risk engine 116 integrates the data information available in the insights database 119, validated data 111 from historic reservoir and well performance, and in relation to the established business goals 109. The risk engine verifies whether reservoir health indicators will impact the compliance of production targets, sustainability of production, cost compliance, and other goals. The risk engine 116 uses Bayesian inference rules to determine whether certain conditions triggered from the previous results as indicated in the insights database 119 and in the context of reservoir and well conditions 111 are potential reservoir risks. The risk engine 116 assigns severity and potential loss associated to the identified risk. The identified risks are captured and or updated in the risk registry 120.

Claims

Claims l. Integrated reservoir management system (l) for the management and control of hydrocarbon reservoirs, the system comprising: a. a data quality management engine (no), capable of checking data from
multiple real-time, legacy and static data sources (100) for consistency and for substituting poor quality data, and capable of generating a database for validated data (ill); b. a data-driven model (113), capable of providing integrated reservoir-wells- facilities prediction in response to the validated data (111) through a calibration engine (112), and capable of generating forecasts (117) in response to a performance optimization engine (121); c. an analytic calculation engine (114), capable of calculating reservoir health indicators (RHI) and storing those reservoir health indicators in the RHI database (118); d. a diagnostic engine (115), capable of analyzing the reservoir health indicators (118) and the validated data (111) to discover features and to associate those features to reservoir health, and storing conditions of such reservoir health features in a database for insights (119); e. a risk engine (116), capable of identifying reservoir management risks by
analyzing the validated data (111) and the insights (119), and capturing or updating the identified risks in a risk registry (120); f. a performance optimization engine (121), capable of searching for optimum reservoir performance scenarios by interrogating the data-driven model (113) and providing results scenarios for a recommender engine (122); and g. a recommender engine (122), capable of assimilating scenarios from the
performance optimization engine (121), insights database (119) and risk registry (120), comparing reservoir health indicators against established industry benchmark metrics and in response thereof recommending a set of actions (123), recommending a set of surveillance requirements (124) and recommending a set of ranked opportunities (125).
2. Integrated reservoir management system according to claim 1, wherein the analytic calculation engine (114) uses validated data (111), data derived from data-driven models (113) and forecasts (117).
3. Integrated reservoir management system according to one of the claims 1 to 2, wherein the analytic calculation engine (114) uses data-driven models (113), which are self-calibrated in response to the validated data (111).
4. Integrated reservoir management system according to one of the claims 1 to 3, wherein the reservoir health indicators comprise: a) visualizations of subsurface characterization maps; and/or
b) 2D and / or 3D scatter cross plots; and/ or
c) historic and/or simulated production/injection rates/ volumes; and/or d) reservoir performance diagnostic plots; and/ or
e) pressure and saturation maps; and/or
f) production injection streamlines; and/or
g) production/injection rates and pressure trends; and/or
h) recovery factors; and/ or
i) water or gas flood sweep efficiency; and/ or
j) fluid ratios, including water cut (WCT) and gas-oil ratio (GOR); and/or k) well and reservoir allocation factors; and/ or l) injection efficiency; and/or
m) voidage replacement ratio (VRR) performance; and/or
n) reservoir model uncertainty; and/ or
o) allocated production volume; and/ or
p) well and facility availability; and/ or
q) reservoir activity costs; and/ or
r) reservoir activity predictability
5. Integrated reservoir management system according to one of the claims l to 4, wherein the recommender engine (122), is further capable of generating
recommended actions (123) based on a comparison of reservoir health indicators (118) against established industry benchmark metrics including reservoir analogue and case data (108).
6. Integrated reservoir management system according to one of the claims 1 to 5, wherein the recommender engine (122), is further capable of generating a set of opportunities (125) in respect to a reservoir based on a comparison of reservoir health indicators (118) against established industry benchmark metrics.
7. Integrated reservoir management system according to claim 6, wherein the
recommender engine (122), assesses and ranks the opportunities by using established industry benchmark metrics including reservoir analogue and case data (108) and provided business goals (109).
8. Integrated reservoir management system according to one of the claims 1 to 7, wherein the recommender engine (122) uses at least one automated case management engine (129) capable of retrieval and reuse of past similar cases, which operates by reference to similarity index computation.
9. Integrated reservoir management system according to claim 8, wherein the past similar cases in the case management engine (129) are previously selected and categorized by a computer-algorithm in response to a combination of physical models, established industry standard benchmark metrics and a set of validated case histories in a case database (108).
10. Integrated reservoir management system according to one of the claims l to 9, wherein the performance optimization engine (i2i)uses reservoir health indicators (118) and forecast data (117) for generating opportunities of reservoir performance optimization, which are further analyzed by the recommender engine (122).
11. Integrated reservoir management system according to one of the claims 1 to 10, further comprising a business process management engine (138), which is capable of enabling process governance.
12. Integrated reservoir management system according to claim 11, wherein the
business process management engine (138) is capable of: a. automatically monitoring and controlling workflows for reservoir review by exemption in tight integrations with the visualization dashboards (128); and/or
b. processing, synthesizing and enabling interpretation of large volume data and reservoir data types; and/or
c. automatically identifying, registering, initiating and tracking actions for establishing if reservoirs are performing as per previous established plan; and/or
d. automatically identifying, registering, initiating and tracking actions to optimize business, reservoir and well performance; and/ or
e. automatically linking decisions, analysis and insights from multiple
disciplines; and/or
f. automatically adding weights to the decisions, analysis and insights from multiple disciplines in response to case accuracy; and/ or
g. automatically enabling opportunity assessment and ranking; and/or h. automatically enabling decision making.
13. Integrated reservoir management system according to one of the claims 1 to 12, wherein the integrated reservoir management system (1) is portable and can be replicated and deployed from one asset to another, assisted by the process configurator (139), while keeping the overall control on the standard process through the business process management engine (138).
14. Integrated reservoir management system according to one of the claims 1 to 13, wherein the integrated reservoir management system provides a role-based integrated visualization and decision support (128) to all system stakeholders (130- 137)·
15. Integrated reservoir management system according to claim 14, wherein the
integrated visualization and decision support (128) is capable of: a. Automatically extracting data stores in all system data sources (100-108, 111, 117-120, 123-125) and presenting those to the right stakeholder (130- 137);
b. Analyzing data by exception;
c. Automatically generating standard and ad-hoc reports; and
d. Triggering workflows for reservoir review by exemption.
16. Integrated reservoir management system according to one of the claims 1 to 15, wherein the integrated reservoir management system provides process key performance indicators (KPI’s) (126) to allow continuous process improvement monitoring.
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