Source: http://www.google.com/patents/US7963327?dq=5,742,768
Timestamp: 2014-08-31 11:34:53
Document Index: 99017892

Matched Legal Cases: ['Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61']

Patent US7963327 - Method for dynamically assessing petroleum reservoir competency and ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsMethods for accurately assessing the condition of a petroleum reservoir and designing and implementing a plan of action to increase production and recovery of petroleum from the reservoir. Information is gathered using a unique set of metrics and information gathering techniques and analyzed in a targeted...http://www.google.com/patents/US7963327?utm_source=gb-gplus-sharePatent US7963327 - Method for dynamically assessing petroleum reservoir competency and increasing production and recovery through asymmetric analysis of performance metricsAdvanced Patent SearchPublication numberUS7963327 B1Publication typeGrantApplication numberUS 12/392,891Publication dateJun 21, 2011Filing dateFeb 25, 2009Priority dateFeb 25, 2008Also published asUS20110168391Publication number12392891, 392891, US 7963327 B1, US 7963327B1, US-B1-7963327, US7963327 B1, US7963327B1InventorsNansen G. Saleri, Robert M. ToronyiOriginal AssigneeQRI Group, LLCExport CitationBiBTeX, EndNote, RefManPatent Citations (7), Non-Patent Citations (13), Referenced by (6), Classifications (9), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetMethod for dynamically assessing petroleum reservoir competency and increasing production and recovery through asymmetric analysis of performance metricsUS 7963327 B1Abstract Methods for accurately assessing the condition of a petroleum reservoir and designing and implementing a plan of action to increase production and recovery of petroleum from the reservoir. Information is gathered using a unique set of metrics and information gathering techniques and analyzed in a targeted fashion by properly weighting the data in the context of the particular reservoir and goals of the producer. A reservoir rating is generated using asymmetric analysis of metrics and then used to formulate a plan of action. Production architecture (e.g., number, location and manner of constructing oil and injector wells) is then constructed according to the plan of action. Reservoir performance can be continuously monitored and used to verify production and recovery goals and/or provide triggers or alarms to alter production equipment.
CROSS REFERENCE TO RELATED APPLICATIONS This application claims the benefit of the following: U.S. Provisional Application No. 61/031,167, filed Feb. 25, 2008, and entitled �METHOD FOR DYNAMICALLY ASSESSING PETROLEUM RESERVOIR COMPETENCY THROUGH ASYMMETRIC ANALYSIS OF PERFORMANCE METRICS�; U.S. Provisional Application No. 61/101,008, filed Sep. 29, 2008, and entitled �ASSESSING PETROLEUM RESERVOIR RESERVES AND POTENTIAL FOR INCREASE�; U.S. Provisional Application No. 61/101,024, filed Sep. 29, 2008, and entitled �ASSESSING PETROLEUM RESERVOIR PRODUCTION RATE THROUGH PRODUCTION GAIN INDEX�; and U.S. Provisional Application No. 61/154,503, filed Feb. 23, 2009, and entitled �METHOD OF ASSESSING THE QUALITY OF RESERVOIR MANAGEMENT�. The disclosures of the foregoing applications are incorporated herein in their entirety.
BRIEF SUMMARY OF THE INVENTION The present invention seeks to overcome existing technical, economic and institutional impediments that reduce production and recovery from a petroleum reservoir by more accurately assessing the actual condition of an existing reservoir and implementing an intelligent plan of action in order to increase short-term production rates and long-term recovery of petroleum from the reservoir. It does so by gathering information using a unique set of metrics and information gathering techniques and analyzing the gathered information in a targeted fashion by properly weighting the data in the context of the particular reservoir in question and the goals of the producer.
All hydrocarbon assets carry an individual �DNA� reflective of their subsurface and surface features. However, conventional methods do not provide useful tools for properly understanding the unique features and needs of each particular petroleum reservoir. The disclosed method provides an enabling tool for developing and applying extraction methods which are optimally designed to the specifications of each individual petroleum reservoir. Its success in arriving at optimal solutions derives from its ability to filter out non-critical parameters and identify the specific reasons for reservoir underperformance. It assists in incrementally increasing both production and reserves over and above levels being achieved using standard industry techniques.
The invention analyzes the gathered information and accurately assesses the condition of a given reservoir by appropriately weighting the various data points. The process of weighting different data points with greater or lesser emphasis is referred to �asymmetric assessment�. There are certain metrics, typically the leading indicators, which are more useful than others (e.g., lagging indicators) in realistically assessing the present and future condition of a petroleum reservoir. Moreover, the manner in which certain metrics are weighted may depend on the particular reservoir in question and/or the specific performance goals of the producer.
A plan of action is implemented in order to increase short-term production and/or long-term recovery (e.g., proven reserves). The plan of action may include one or more of the following: (1) modifying and/or stimulating one or more existing wells, (2) constructing new wells, (3) injection of pressurized fluids and/or gas in a more intelligent and strategic manner, and (4) shutting or slowing down production by one or more existing wells. In general, it is beneficial to (1) maximize contact between the well bore and reservoir, (2) reduce gas-to-oil ratios and/or water cuts and/or draw-down pressures among adjacent and/or similarly situated and/or similarly designed wells, and (3) optimize extraction rates to more closely correspond to surrounding pore displacement efficiencies and well-bore replenishment locations.� When the producing wells of a reservoir are operating in an optimized manner, short-term production is increased and long-term recovery is maximized.
Finally, the petroleum reservoir may be monitored to ensure compliance with design and production goals, e.g., as set by RCAA�. Alarms or trigger points may be provided which, when exceeded such as by falling below a specified minimum or exceeding a specified maximum, call for a response. The response may be a notification to a manager or other interested party, or it may be an automatic adjustment to some production parameter.
The present invention is directed toward a comprehensive method for enhancing ongoing production and ultimate recovery of petroleum from a reservoir. This method may be referred to as Reservoir Competency Asymmetric Assessment� (or RCAA�). RCAA� includes several closely interrelated sub-methods or modules that are employed in concert and sequentially. They are (i) analyzing and diagnosing the specific and unique features of a reservoir (i.e., its �DNA�) using targeted metrics, (ii) designing a plan of action for maximizing current production and ultimate recovery from the reservoir, (iii) implementing the plan of action so as to increase current production and ultimate recovery, and (iv) monitoring or tracking the performance of the petroleum reservoir using targeted metrics and making adjustments to production parameters, as necessary, to maintain desired productivity and recovery.
According to one embodiment, implementation of RCAA� spans six interweaving and interdependent tracks: i) Knowledge Systems; ii) Q6 Surveys; iii) Deep Insight Workshops; iv) Q-Diagnostics; v) Gap Analysis; and vi) Plan of Action. The information gathered from these tracks is integrated using modern knowledge-sharing mediums including web-based systems and communities of practice. A comprehensive chart showing the conceptual and temporal interrelation of the six tracks is illustrated in FIGS. 1A and 1B (i.e., two halves of one chart). While the overall business model includes both technological and non-technological means for gathering the relevant information, the method cannot be implemented without the use of physical processes and machinery for gathering key information. Moreover, implementing a plan of action involves computerized monitoring of well activity. And enhanced reservoir performance results in a physical transformation of the reservoir itself.
Monitoring the performance of the reservoir before, during and/or after implementation of a plan of action involves the use of a computerized system (i.e., part of the �control room�) that receives, analyzes and displays relevant data (e.g., to and/or between one or more computers networked together and/or interconnected by the internet). Examples of metrics that can be monitored include 1) reservoir pressure and fluid saturations and changes with logging devices, 2) well productivity and drawdown with logging devices, fluid profile in production and injection wells with logging devices, and oil, gas and water production and injection rates. Relevant metrics can be displayed on the internet. Web based systems can share such data. FIGS. 3A-3D illustrate exemplary �dashboards� that can be used to graphically display certain metrics (e.g., leading and lagging) compiled from ongoing data sampling of producing wells. The dashboards can provide a quick visual diagnostic tool to assess past and future performances.
Individual computer systems within monitoring system 400 (e.g., main computer system 402 and remove computers 420) can be connected to a network 430, such as, for example, a local area network (�LAN�), a wide area network (�WAN�), or even the Internet. The various components can receive and send data to each other, as well as other components connected to the network. Networked computer systems and computers themselves constitute a �computer system� for purposes of this disclosure.
RCAA� utilizes a variety of reservoir performance metrics, including both leading and lagging indicators, that can provide information regarding the �DNA� of a reservoir. In addition, it utilizes unit development metrics, workload metrics, business plan metrics, and stretch goals. These indicators and metrics typically utilize specialized terminology and variables that may not be readily understood by the lay person. The following nomenclature and definitions are provided to clarify and enhance understanding of the disclosed metrics and how they may relate to reservoir properties.
The methodologies and definitions utilized by RCAA� are intended to be consistent with industry standards and practices. The key standard for the definition of Proved Reserves is the United States Securities and Exchange Commission Regulation S-X (17 CFR 210.4-10-11/88). For Probable and Possible reserves and for Contingent Resources the reference standard is Petroleum Reserves and Resources Classification, Definitions, and Guidelines of Society of Petroleum Engineers (SPE), American association of Petroleum Geologists (AAPG), World Petroleum Congress (WPC) and Society of Petroleum Evaluation Engineers (SPEE) (2006).
The reservoir performance metrics utilized in RCAA� are generally classified as leading indicators, lagging indicators, unit development metrics, workload metrics, business plan metrics, and stretch goals. In general, leading indicators are more predictive of future productivity and/or recovery than lagging indicators. Lagging indicators may, however, provide an accurate accountability tool. Both types of indicators can be used to identify gaps between reality and the ideal and help improve production and recovery.
The following are examples of leading indicators that can be used in RCAA�. A first leading indicator is the �Dead Well Index�. A related leading metric is the �Dead Wells Gradient�. The Dead Well Index is determined by the number of dead wells divided by the sum of both dead and active producers. The ratio is therefore dimensionless. The Dead Wells Gradient is the normalized yearly rate of change of dead well index: (DWI), (DWI1−DWI0)/DWI0, yr−1. FIG. 5A is a bar graph that shows an exemplary year-to-year comparison of the Dead Well Index. It also includes a line showing the Dead Wells Gradient.
A second leading indicator is the �Gas Oil Ratio� (GOR). A related leading metric is the �Gas Oil Ratio Gradient�. The Gas Oil Ratio is the producing ratio of gas to oil volume: (R)=ΔGp/ΔNp, scf/stb. The Gas Oil Ratio Gradient is the rate of change of the Gas Oil Ratio: GOR=R1−R0, yr−1. FIG. 5B is a bar graph that shows an exemplary year-to-year comparison of the Gas Oil Ratio. It also includes a line showing the Gas Oil Ratio Gradient.
A third leading indicator is the �Reservoir Pressure Change�. The Reservoir Pressure Change is the difference in annual volumetric weighted average reservoir pressure: psi-yr−1. FIG. 5C is a bar graph that shows an exemplary year-to-year comparison of the Reservoir Pressure Change.
A fourth leading indicator is the �Oil Decline Rate�. A related leading metric is the �Oil Decline Rate Gradient�. The Oil Decline Rate is the normalized change in annual oil volume: (C)=(ΔNP0−ΔNp1)/ΔNp1, yr−1. The Oil Decline Rate Gradient is the annual change in Oil Decline Rate, or C1−C0, yr−2. FIG. 5D is a bar graph that shows an exemplary year-to-year comparison of the Oil Decline Rate. It also includes a line showing the Oil Decline Rate Gradient.
A fifth leading indicator is the �Waterflood Efficiency�. A related leading metric is the �Waterflood Efficiency Gradient�. The Waterflood Efficiency is defined as (Ew)=Np/NM/WC and is dimensionless. The Waterflood Efficiency Gradient is the normalized yearly rate of change in waterflood efficiency: (Ew)=EW1−EW0, yr−1. FIG. 5E is a bar graph that shows an exemplary year-to-year comparison of the Waterflood Efficiency. It also includes a line showing the Waterflood Efficiency Gradient.
A sixth leading indicator is the �Water Cut�. A related leading metric is the �Water Cut Gradient�. The Water Cut is the producing ratio of water to liquid volume and is therefore dimensionless: (WC)=ΔWp/(ΔNp+ΔWp). The Water Cut Gradient is the normalized yearly rate of change in water cut, or WC1−WC0, yr−1. FIG. 5F is a bar graph that shows an exemplary year-to-year comparison of the Water Cut. It also includes a line showing the Water Cut Gradient.
The following are examples of lagging indicators that can be used in RCAA�. A first lagging indicator is the �Average Producer Liquid Rates�, which includes both �Oil Rate� and �Water Rate�. The Oil Rate is the producing oil rate on a well basis: (qo)=ΔNp/365/Number of Active Producers, bpd. The Water Rate is the producing water rate on a well basis: (qw)=ΔWp/365/Number of Active Producers, bpd. FIG. 6A is a bar graph that shows an exemplary year-to-year comparison of the Oil Rate and Water Rate.
A second lagging indicator is the �Depletion Rate�. A first type of Depletion Rate is the �Expected Ultimate Recovery (EUR) Depletion Rate�, which equals ΔNp/EUR, and is dimensionless. A second type of Depletion Rate is the �Proved Reserves (1P) Depletion Rate� and is also dimensionless: 1P Depletion Rate=ΔNp/1P. FIG. 6B is a bar graph that shows an exemplary year-to-year comparison of the Expected Ultimate Recovery (EUR) Depletion Rate and 1P Depletion Rate.
A third lagging indicator is the �Depletion State�. A first type of Depletion State is the �Expected Ultimate Recovery Depletion State� and is dimensionless: (NDPe)=Np/EUR. A second type of Depletion State is the �Mobile Original Oil Initially in Place (OIIP) Depletion State� and is also dimensionless: (NPDm)=Np/ NM. A third type of Depletion State is simply the OIIP Depletion State. FIG. 6C is a bar graph that shows an exemplary year-to-year comparison of the Expected Ultimate Recovery Depletion State, the Mobile OIIP Depletion State, and the OIIP Depletion State.
A fourth lagging indicator is the �Dimensionless Pressure Drawdown�. The Dimensionless Pressure Drawdown is the median pressure drawdown divided by the median ideal vertical pressure drawdown, and is dimensionless: (ΔPdd(DM))=ΔPdd/ΔPdd(IV))M. FIG. 6D is a bar graph that shows an exemplary year-to-year comparison of the Dimensionless Pressure Drawdown.
A fifth lagging indicator is the �Dimensionless Productivity Index�. The Dimensionless Productivity Index is the median Productivity Index (PI) divided by the median ideal vertical Productivity Index and is dimensionless: (PI/PIIV)M. FIG. 6E is a bar graph that shows an exemplary year-to-year comparison of the Dimensionless Productivity Index.
A sixth lagging indicator is the �Dimensionless Injectivity Index�. The Dimensionless Injectivity Index is the median Injectivity Index (II) divided by the median ideal vertical Injectivity Index and is dimensionless: (II)DM=(II/IIIV)M. FIG. 6F is a bar graph that shows an exemplary year-to-year comparison of the Dimensionless Injectivity Index.
A seventh lagging indicator is the �Gas Rate�. The Gas Rate is the Producing Gas Rate: (qg)=ΔGp/365, mmsfcd. FIG. 6G is a bar graph that shows an exemplary year-to-year comparison of the Gas Rate.
An eighth lagging indicator is the �Liquid Rate�. A first type of Liquid Rate is the �Maximum Efficient Rate� (MER), mbd, and is the reservoir off-take rate above which will occur significant reduction in estimated ultimate recovery. A second type of Liquid Rate is the �Oil Rate�, which is the producing oil rate: (qo)=ΔNp/365, mbd. A third type of Liquid Rate is the �Water Rate�, which is the producing water rate: (qw)=ΔWp/365, mbd. FIG. 6H is a bar graph that shows exemplary year-to-year comparisons of the MER, oil rate and water rate.
A ninth lagging indicator is the �Pressure Gradient�. The Pressure Gradient is the median pressure difference across a distance, e.g., the pressure difference between a producer and injector divided by the distance, or Δp/L, psi/ft.
A tenth lagging indicator is the �Productivity Index Gradient�. The Productivity Index Gradient is the change in the median productivity index as a result of reservoir compaction: 1−(PIM1/PIM0), bpd/psi.
An eleventh lagging indicator is �Rate Restrictions�. Rate Restrictions are the sum of wellhead potential rates minus the sum of restricted rates, mbd. A variation includes Dimensionless Rate Restrictions, which are the effective rate restrictions divided by MSC, dimensionless.
A twelfth lagging indicator is the �Recovery Efficiency�. A first type of Recovery Efficiency is the �Oil Recovery Factor�: (ER)=EUR/N, dimensionless. A second type of Recovery Efficiency is the �Mobile Oil Depletion Efficiency�: (ERM)=EUR/NM, dimensionless. A third type of Recovery Efficiency is the Theoretical Maximum Recovery Factor: (ERT)=NM/N, dimensionless.
A thirteenth lagging indicator is the �Transmissibility Index�. The Transmissibility Index is the permeability-cross-sectional area product divided by distance: kA/L, and md-ft.
A fourteenth lagging indicator is the �Voidage Replacement Ratio� (VRR). A first type of Voidage Replacement Ratio is the �Surface Voidage Replacement Ratio�, which is the VRR at surface conditions of pressure and temperature: ΔWi/(ΔNp+ΔWp), dimensionless. A second type of Voidage Replacement Ratio is the �Reservoir Voidage Replacement Ratio�, which is the VRR at reservoir conditions of pressure and temperature: (ΔWi�Bw)/((ΔNp�Bo)+(ΔWp�Bw)), dimensionless. FIG. 6I is a bar graph that shows exemplary year-to-year comparisons of the Surface Voidage Replacement Ratio and Reservoir Voidage Replacement Ratio.
A first unit development metric is the �Cost Factor�. A first type of Cost Factor is the �Drilling Cost Factor�, which is the average annual initial oil production rate divided by the drilling and completion cost, bpd/$. A second type of Cost Factor is the �Workover Cost Factor�, which is the average annual initial oil production rate divided by the workover cost, bpd/$. FIG. 7A is a bar graph that shows exemplary year-to-year comparisons of the Drilling Cost Factor and Workover Cost Factor.
A second unit development metric is the �Efficiency Factor� (or Rig Efficiency Factor). A first type of Efficiency Factor is the �Drilling Efficiency Factor�, which is the average annual initial oil production rate divided by the number of days required to drill and complete a well, bpd/rig-days. A second type of Efficiency Factor is the �Workover Efficiency Factor�, which is the average annual initial oil production rate divided by the number of days required to workover a well, bpd/rig-days. FIG. 7B is a bar graph that shows exemplary year-to-year comparisons of the Drilling Efficiency Factor and Workover Efficiency Factor.
A third unit development metric is the �Median Reservoir Contact�. A first type of Median Reservoir Contact involves producers, which measures the median producer reservoir contact, ft. A first type of Median Reservoir Contact involves injectors, which measures the median injector reservoir contact, ft. FIG. 7C is a bar graph that shows exemplary year-to-year comparisons of Median Reservoir Contact for both producers and injectors.
A third workload metric is the �Well Count�. A first type of Well Count is �New Wells�, which is the number of new wells drilled for the year, annual count. A second type of Well Count is �Active (Horizontal/Lateral/Slant)�, which is the mean number of active non-vertical producers operating for the year, annual count. A third type of Well Count is �Active Total�, which is the mean number of all active producers operating for the year, annual count. FIG. 8C is a bar graph that shows exemplary year-to-year comparisons of Well Count for each of New Wells, Active (Horizontal/Lateral/Slant), and Active Total.
A first business plan metric is �Fluid Rates�. A first type of Fluid Rate is the �Oil Rate�, which is the forecast oil rate for a five year business planning cycle, mbd. A second type of Fluid Rate is the �Water Rate�, which is the forecast water rate for a five year business planning cycle, mbd. A third type of Fluid Rate is the �Water Cut�, which is the forecast water cut for a five year business planning cycle, mbd. FIG. 9A is a bar graph that shows exemplary year-to-year comparisons of Fluid Rates for each of Oil Rate, Water Rate, and Water Cut.
A first business plan metric is �Producer Drilling Requirements�. A first type of Producer Drilling Requirement is �New Wells�, or the total number of producers required to provide the forecast oil rate, annual count. A second type of Producer Drilling Requirement is �Sidetracks�, or the total number of sidetracks of existing producers to provide the forecast oil rate, annual count. FIG. 9B is a bar graph that shows exemplary year-to-year comparisons of Producer Drilling Requirements for both New Wells and Sidetracks.
A first stretch goal is �Components�. A first type of Components stretch goal is �Historical�: the last five years of performance are provided for perspective. A second type is �Forecast�: a five year business plan forecast that considers the current rate of implementation of new technologies and best practices. A third type is �Goal�: a five year forecast that considers a 10% acceleration in the implementation of new technologies and best practices.
A second stretch goal is �Production Development Cost�. The Production Development Cost is the cost to drill and complete a well divided by its total cost, $/bpd. FIG. 10A is a bar graph that shows exemplary year-to-year comparisons and forecasts for Production Development Cost, particularly historical, forecast and goal.
A third stretch goal is �Voidage Replacement Ratio� (VRR). One type is the Surfact VRR, which is the VRR at surface conditions: ΔWi/(ΔNp+ΔWp), dimensionless. FIG. 10B is a bar graph that shows exemplary year-to-year comparisons and forecasts for Surface Voidage Replacement Ratio, particularly historical, forecast and goal.
A fourth stretch goal is �Water Cut�. The Water Cut is the producing ratio of water to liquid volume: ΔWp/(ΔNp+ΔWp), dimensionless. FIG. 10C is a bar graph that shows exemplary year-to-year comparisons and forecasts for Water Cut, particularly historical, forecast and goal.
RCAA� integrates a wide variety of information; however, its success in arriving at optimal solutions derives from its ability to filter out non-critical parameters and recognize fundamental areas of reservoir underperformance. This is achieved by means of a set of metrics designated as �Integrative Metrics�. Integrative Metrics (also called �Special Metrics�) include:
1) Reservoir Management Rating (RMR�); 2) Production Gain Index (PGI�); and 3) Recovery Deficiency Indicator (RDI�). Integrative Metrics provide a numerical assessment of critical reservoir performance parameters which, in turn, become the screening basis for planning and implementation of optimal solutions. As an example, a reservoir which scores poorly on RDI� points to the fact that its recovery design is mismanaged. Case in point: a reservoir being depleted without the benefit of a pressure-maintenance or secondary-recovery process will have a low RDI� score. Remedial actions will need to consider a secondary-recovery (e.g., waterflood). The Integrative Metrics will point in this direction very rapidly. As a result, correct application of RCAA� will result in increased recoveries and production rates while providing superior utilization of capital.
RMR� is a structured approach for assessing the quality of reservoir management used in the recovery of hydrocarbons from a particular reservoir. It involves the use and analysis of a unique set of metrics, indices and quality measures as they relate to the physical state of the reservoir. the positioning and operation of wells (e.g., producers and injectors), and how the reservoir is managed (i.e., the long-term plan governing production and recovery). A detailed description of RMR� is set forth in U.S. Provisional Application No. 61/154,503 which was filed Feb. 23, 2009, and entitled �METHOD OF ASSESSING THE QUALITY OF RESERVOIR MANAGEMENT,� the disclosure of which is incorporated by specific reference.
In order to implement RMR�, a field is evaluated and judged (scored) on the basis of six categories using a letter grading system (A, B, C, and D). [See Table 2 below]. The letter grade assigned to the reservoir management provides a quick tool for judging the potential for increasing petroleum production and reserves.
DEFINITIONS OF ACRONYMS FOR RMR� METRICS The following is a list of definitions of the acronyms used in connection with metrics utilized in RMR�:
CTI: Completion Technology Index DEI: Displacement Efficiency Index DMI: Drawdown Management Index DPRI: Displacement Process Risk Index DR: Displacement Risk DTI: Drilling Technology Index EUR: Estimated Ultimate Recovery FDI: Field Depletion Index FPDI: Field Productivity Deficiency Index GC: Geological Complexity GMI: Gas Management Index KMI: Knowledge Management Index OVI: OIIP/GIIP Verification Index PI: Production Index PMI: Pressure Management Index PPAI: Production Plan Achievement Index PSI: Plateau Sustainability Index RDI: Recovery Deficiency Indicator RDTI: Reservoir Dynamics Technology Index RMF: Risk Management Factor RMI: Risk Mitigation Index (RMI) RVI: Reserves Verification Index SEI: Sweep Efficiency Index SPDI: Surveillance Plan Design Index SPII: Surveillance Plan Implementation Index STI: Stimulation Technology Index VAG: Value at Gain VAR: Value at Risk WMI: Water Management Index WRDI: Well Rate/Drawdown Index SCORING A management score is assigned using the following weighting factors:
Categories Weighting % Reservoir Management Design 25 Reserves Appreciation 25 Development & Operating Plan 20 Reservoir Surveillance 10 Technology Application 15 Knowledge Management 5 TOTAL 100% The foregoing weighting factors are used to generate a Reservoir Management Rating� (RMR�) Matrix, which identifies subcategories of metrics used to evaluate the competency of reservoir management within the various categories. The metrics are, in turn, used to generate a score. The Reservoir Management Rating� (RMR�) Matrix is illustrated below in Table 1.
A scoring scale for assessing reservoir management according to RMR� is illustrated below in Table 2.
The Reservoir Management Design has a weighting of 25% relative to the overall Reservoir Management Rating�. Important issues are 1) whether there is a Reservoir Management Design, 2) whether the design includes reservoir management tenets, and 3) whether the tenets been applied in the correct fashion. As set forth in Table 1 above, the Reservoir Management Design includes five subcategories, which are equally weighted relative to each other
The metric for Recovery Design is the Recovery Deficiency Indicator (RDI�). A more detailed description of RDI� is disclosed in U.S. Provisional Application No. 61/101,008, which was filed Sep. 29, 2008, and entitled �ASSESSING PETROLEUM RESERVOIR RESERVES AND POTENTIAL FOR INCREASE�, the disclosure of which is incorporated by specific reference. The RDI� is defined or determined as follows:
RDI�=RE/IRE*100
IRE (Ideal RE)=EA*EI*ED=1*1*ED=ED where:
The metric for Displacement Process Risk is the Displacement Process Risk Index (DPRI), which is defined or determined below. (Proviso: IF the downside risk of recovering 2P reserves has not been determined, THEN assign �60� to this subcategory and continue to the next subcategory.)
The metric for Plateau Sustainability is the Plateau Sustainability Index (PSI), which is defined or determined below, with further reference to Table 9. (Proviso: IF the field depletion plan does not allow for plateau production, THEN assign �60� to this subcategory and continue to the next subcategory.)
The Reserves Appreciation has a weighting of 25% relative to the overall Reservoir Management Rating�. Important issues are 1) whether the components of reserves determination been validated, 2) whether the risks to achieving and appreciating reserves been identified, and 3) whether contingency plans been prepared. As set forth in Table 1 above, the Reserves Appreciation includes five subcategories, which are equally weighted relative to each other
The metric for Sweep Efficiency is the Sweep Efficiency Index (SEI). The Sweep Efficiency Index is defined or determined as follows (Proviso: IF the reservoir is under depletion or compression drive, THEN assign �NA� to this subcategory and continue to the next subcategory):
The metric for Displacement Efficiency is the Displacement Efficiency Index (DEI). The Sweep Efficiency Index is defined or determined with reference to Table 11. (Proviso: IF the reservoir is under depletion or compression drive, THEN assign �NA� to this subcategory and continue to the next subcategory.)
FIG. 1 is a graph which illustrates how overall petroleum reserves of reservoir can be increases through risk mitigation as a result of implementation of RMR�.
The Development and Operating Plan has a weighting of 20% relative to the overall Reservoir Management Rating�. An important issue is whether the desired design goals and operating targets are being achieved. As set forth in Table 1 above, the Development and Operating Plan includes six subcategories, which are equally weighted relative to each other
PPAI=Variance1-year+Variance5-year where:
The metric for Pressure Management is the Pressure Management Index (PMI). The Pressure Management Index is defined or determined as follows (Proviso: IF the reservoir is in its initial transient period, THEN assign �NA� to this subcategory and continue to the next subcategory):
The metric for Gas Management is the Gas Management Index (GMI). The Gas Management Index is defined or determined with reference to Table 15. (Proviso: IF there is no gas cap or gas injection, THEN assign �NA� to this subcategory and continue to the next subcategory.)
The category Reservoir Surveillance has a weighting of 10% relative to the overall Reservoir Management Rating�. An important issue is how good is the Surveillance Program (tracking the right parameters the right way at the right times). As set forth in Table 1 above, Reservoir Surveillance includes two subcategories, which are equally weighted relative to each other
The category Technology Application has a weighting of 15% relative to the overall Reservoir Management Rating�. Important issue are 1) whether the most appropriate technologies are being implemented to achieve the Recovery Design goals and 2) how ready and receptive is the reservoir owner or manager in considering state-of-the-art or alternate appropriate technologies. As set forth in Table 1 above, the category Technology Application includes four subcategories, which are equally weighted relative to each other.
The category Knowledge Management has a weighting of 5% relative to the overall Reservoir Management Rating�. Important issue are 1) what is the organization's commitment to knowledge sharing initiatives, 2) whether data quality is complete, uniform, and consistent, while maintaining integrity and lacking duplication, 3) whether the owner or manager has access to virtual collaboration environments and how well utilized they are, and 4) whether the owner or manager has access to daily, monthly, or annual reports critical to your operations.
All or part of the RMR� method may be implemented using a conventional computer system comprised of one or more processors, volatile memory, non-volatile memory or system storage, and one or more input-output devices. An example is computer system 400 discussed above and illustrated in FIG. 4.
According to one embodiment for implementing RMR�, a method of assessing the quality of reservoir management used in the recovery of petroleum from a reservoir includes: 1) establishing reservoir management metrics for each of the following categories: a) reservoir management design, b) reserves appreciation, c) development and operating plan, d) reservoir surveillance and monitoring, e) technology application, and f) knowledge management; 2) weighting the reservoir management metrics according to the categories to which they belong; 3) obtaining data relating to the reservoir management metrics, at least some of the data being generated by at least one of (i) measuring a physical property of one or more producing oil wells and/or injector wells of the reservoir, (ii) taking and analyzing one or more core samples from the reservoir, or (iii) establishing a relationship between one or more different types of data from (i) or (ii); 4) generating the reservoir management metrics from the data; and 5) determining a reservoir management rating for the petroleum reservoir based on the reservoir management metrics, the reservoir management rating relating to at least one of production or recovery of petroleum from the reservoir.
b. Production Gain Index�
The Production Gain Index� (PGI�) is a novel leading indicator and metric designed to quickly assess the potential for increases in productivity of an operating petroleum reservoir. A detailed description of PGI� is set forth in U.S. Provisional Application No. 61/101,024, filed Sep. 28, 2008, and entitled �ASSESSING PETROLEUM RESERVOIR PRODUCTION RATE THROUGH PRODUCTION GAIN INDEX,� the disclosure of which is incorporated herein by specific reference. The Production Gain Index for a petroleum reservoir is defined as:
PGI = ∑ ⁢ Δ ⁢ ⁢ q A ∑ ⁢ q Old A related index, the Global Productivity Index (GPI�), is defined as
GPI = ∑ ⁢ J New ∑ ⁢ J Old wherein,
ΣΔqA=net actual production gain, stbpd (standard barrels produced per day); ΣqOld=sum of current oil rates for existing producers, stbpd ΣJNew=sum of productivity indices of all producers post project deployment, stbpd/psi; ΣJOld=Sum of productivity indices of all producers prior project deployment, stbpd/psi; and CE=Interference factor, which is an empirically derived factor that accounts for the loss in the aggregate production gain due to well interference. Its formula is as follows: C E = ( 1 - log 1 ⁢ o ⁢ ∑ ⁢ J New ∑ ⁢ J Old ) The dimensionless Production Gain Index is based on the petroleum engineering concept of the productivity index (J), which is a measure of the ability of the well to produce. It is defined as the well's stabilized flow rate measured at surface conditions divided by the well's drawdown. Drawdown is the difference in static bottom-hole pressure and stabilized flowing bottom-hole pressure.
PGI = ∑ ⁢ Δ ⁢ ⁢ q A ∑ ⁢ q Old Alternatively, the PGI can be determined by (1) determining or obtaining the interference factor (CE) for a reservoir, (2) determining or obtaining the global productivity index (GPI�), which is the ratio of (a) the sum of productivity indices of all producers post project deployment, stbpd/psi (ΣJNew) and (b) the sum of productivity indices of all producers prior project deployment, stbpd/psi (ΣJOld), and multiplying the interference factor by the difference between the global productivity index (GPI�) and 1, according to the following equation:
∑ ⁢ Δ ⁢ ⁢ q A ∑ ⁢ q Old = C E � ( ∑ ⁢ J New ∑ ⁢ J Old - 1 ) As discussed above, the interference factor is determined according to the following equation:
C E = ( 1 - log 1 ⁢ ⁢ o ⁢ ∑ ⁢ J New ∑ ⁢ J Old ) c. Recovery Deficiency Indicator�
The Recovery Deficiency Indicator� (RDI�) is a new leading indicator and metric that is designed to quickly assess the potential for increases in petroleum recovery from a reservoir. As noted above, the RDI� may form part of the RMR� analysis. A more detailed description of RDI� is disclosed in U.S. Provisional Application No. 61/101,008, which was filed Sep. 29, 2008, and entitled �ASSESSING PETROLEUM RESERVOIR RESERVES AND POTENTIAL FOR INCREASE�, the disclosure of which is incorporated by specific reference. The RDI� is determined by taking the ratio of a reservoir's recovery efficiency (RE), or recovery factor, and its ideal recovery factor (IRE). This is represented as follows:
RE=EA*EV*ED where,
Reservoir deficiency indicator (RDI�) values can be broken into five ranges or reservoir deficiency scores (�RDS�), which can be used to evaluate and highlight degrees of non-conformance and potential actions that can be taken to correct the shortfall in actual recovery compared to ideal recovery. According to one example, the reservoir deficiency scores can be tabulated as shown in Table 24 below:
According to one embodiment, an exemplary process for determining the recovery deficiency indicator (RDI�) and corresponding reservoir deficiency score (RDS) for a producing field or reservoir comprises: (1) determining or obtaining the areal displacement efficiency (EA); (2) determining or obtaining the vertical displacement efficiency (EV); (3) determining or obtaining the pore displacement efficiency (ED); (4) determining the recovery efficiency (RE) based on the areal displacement efficiency (EA), vertical displacement efficiency, and the pore displacement efficiency; (5) determining the ideal recovery efficiency (IRE) by assuming that the areal displacement efficiency (EA) and vertical displacement efficiency (EV) are 100% and setting IRE=ED; (6) determining the recovery deficiency indicator (RDI�) by determining the ratio between the recovery efficiency (RE) and ideal recovery efficiency (IRE); and (7) assigning a reservoir deficiency score (RDS) based on the recovery deficiency indicator (RDI�). All or part of the foregoing process may be implemented using a conventional computer system comprised of one or more processers, volatile system memory, non-volatile system memory or storage, and one or more input-output devices.
III. Implementation of RCAA�
A detailed description of RCAA� is attached as an appendix to U.S. Provisional Application No. 61/031,167 filed Feb. 25, 2008, and entitled �METHOD FOR DYNAMICALLY ASSESSING PETROLEUM RESERVOIR COMPETENCY THROUGH ASYMMETRIC ANALYSIS OF PERFORMANCE METRICS,� the disclosure of which is incorporated herein in its entirety including the appendix thereto (hereinafter referred to as �RCAA document�). The RCAA document includes various sections, including an executive overview and a client SME (subject matter expert) workbook. The executive overview briefly describes the RCAA� and what it seeks to accomplish and includes subsections relating to the preamble, QRI� (Quantum Reservoir Impact) reservoir management model, principal focus areas, and gap analysis. The client SME workbook includes subsections relating to Q6 surveys, knowledge systems, deep insight workshops, Q-diagnostics, gap analysis, and plan of action (see FIG. 1). The various pieces of RCAA� interact together in a synergistic manner in order to maximize the ability knowledgably increase reservoir productivity (i.e., production and reserves).
Continuous monitoring of certain metrics can be provided by �dashboards� which provide real time display of various metrics. A dashboard can provide instant monitoring of many dynamically changing variables at once. They may include triggers or alarms, such as maxima or minima which, when met, may require affirmative steps to alter how production is being carried. These include, for example, closing or opening valves in the well bore, choking or increasing flow rate by adjusting impellers, activating or altering pumps to increase flow rate, making perforations in the pipe to begin removing oil in certain locations in the bore, and stimulation of existing wells, such as by fracking or acidization, to increase the amount of rock through which oil flows.
According to one embodiment consistent RCAA�, there is provided a method of assessing the competency of a petroleum reservoir relative to production and recovery for purposes of initiating a plan of action to increase production and/or recovery, the method comprising: 1) establishing a plurality of reservoir performance metrics that relate to the production and recovery of petroleum from the reservoir; 2) weighting one or more of the reservoir performance metrics more heavily than at least one other of the reservoir performance metrics to facilitate asymmetric analysis of the reservoir performance metrics; 3) obtaining data relating to the reservoir performance metrics, the data being generated by at least one of (i) measuring a physical property of one or more producing oil wells and/or injector wells of the reservoir, (ii) taking and analyzing one or more core samples from the reservoir, or (iii) establishing a relationship between one or more different types of data from (i) or (ii); 4) generating the reservoir performance metrics from the data; and 5) determining a competency rating for the petroleum reservoir based on asymmetric analysis of the reservoir performance metrics, the competency rating relating to at least one of production or recovery of petroleum from the reservoir.
According to one embodiment, the data relating to the reservoir performance metrics are input into a computer, which then analyzes and displays the data in one or more forms, such as spreadsheets and tables (e.g., as illustrated in FIGS. 5-10). The displayed data can be used to assess reservoir competency. In general, the worse an existing reservoir is currently being managed and operated, the more gains can be made through implementation of the RCAA� methodology.
The metrics that are most important in assessing reservoir competency include the leading indicators described above. Examples of useful leading indicators include dead well index, dead well gradient, gas oil ratio, gas oil ratio gradient, reservoir pressure change, oil decline rate, oil decline rate gradient, waterflood efficiency, waterflood efficiency gradient, recovery deficiency indicator, or production gain index. Less useful, but certainly within the scope of RCAA� to utilize are lagging indicators. Examples of useful lagging indicators include average producer liquid rates, oil rate, water rate, depletion rate, expected ultimate recovery depletion rate, 1P depletion rate, depletion state, expected ultimate recovery depletion state, mobile oil initially in place depletion state, dimensionless pressure drawdown, dimensionless productivity index, dimensionless injectivity index, gas rate, liquid rate, maximum efficient rate, pressure gradient, productivity index gradient, rate restrictions, dimensionless rate restrictions, recovery efficiency, oil recovery factor, mobile oil depletion efficiency, theoretical maximum recovery efficiency, transmissibility index, voidage replacement ratio, surface voidage replacement ratio, reservoir voidage replacement ratio.
According to one embodiment, metrics can be selected and weighted according to what is described in the section above relating to RMR�. In general, the asymmetric assessment of reservoir competency helps to understand the specific DNA or state of affairs of the reservoir, which provides insight as to how a plan of action to increase productivity and recovery is to be designed. As more information is learned regarding the reservoir, other metrics may become more or less important to the analysis. The RCAA� allows for distillation of the data. It takes a complex picture that may be meaningless and distills it to a very clear picture. This helps develop a more intelligent and successful plan of action, and provides a tool for executing the plan of action. It acts as a continual guide to the organization.
According to one embodiment, principles relating to �Six Sigma� (6Σ) can be applied to aspects of the reservoir subsurface. The purpose of 6Σ is to identify outliers that are far outside the mean, such as oil producing wells. In many cases, outliers may simply be bad apples suitable for shutdown. However, the outliers might in some cases be the most highly productive oil wells of a reservoir. They might point to the ideal and form the basis for duplication by other oil wells or provide information regarding favorable subsurface conditions in the vicinity of the outlier oil wells. Outliers might be identified, for example, using a productivity gradient metric that compares oil well productivity across the entire reservoir.
In addition, external factors may affect which metrics are most important. These include economic factors (i.e., what is the time horizon of the owner in terms of dollars spent versus dollars earned from an enhanced recovery plan using RCAA�. Another type of external factor includes risk factors. In general, risk factors can be mitigated by properly designing a recovery plan.
A plan of action according to RCAA� is formulated based on the properly gathered, analyzed and weighted data for a particular reservoir. The plan of action constitutes a comprehensive road map with details regarding agreed upon metrics and key performance indicators. Because the plan of action is based on an accurate assessment of the short-, mid- and long-term condition of the reservoir and is tailored to the specific conditions of the reservoir and/or needs of the producer, the plan of action is far more likely to succeed and result in increased short-, mid- and/or long-term production and profits compared to what is possible using conventional methods.
According to one embodiment, designing a plan to increase productivity and/or recovery involves obtaining data from the diagnosis step described above and working with the producer to understand the benefits and limits of one or more possible plans of action. The use of RMR�, for example, will help develop of a rating system that permits a producer to intelligently assess a desirable plan of action. Workshops may be employed to test out different plans of action to determine what works best given the goals of the producer.
As discussed above, establishing a desired production rate and ultimate recovery for the petroleum reservoir generally considers how much a producer wishes to invest in increasing production and recovery of petroleum from the reservoir. To maximize both production and long term productivity, the plan of action or production architecture includes design and placement of at least one maximum contact well having a plurality of branched, at least partially horizontal well bores. This type of well is known as a �maximum reservoir contact� (MRC) well. An exemplary MRC well is illustrated in FIG. 11, and includes a multiple branched well bore 1100, including a pluarality of spaced-apart well bore subsections 1102 that extended generally horizonatally through one or more strata 1104 of the reservoir. The well bore subsections 1102 may also be positioned vertically relative to each other in order to better drain oil found at different reservoir depths. In general, an MRC well is used to better drain oil pockets that are generally fluidly interconnected.
Another aspect of RCAA� is implementation of the plan of action formulated based on the properly gathered, analyzed and weighted data for a particular reservoir. According to one embodiment, the plan of action is designed in consideration of the RMR� and to increase productivity and/or recovery from the reservoir.
Another aspect of RCAA� is monitoring and tracking the performance of a petroleum reservoir, such as one designed or improved according to RCAA�. Again, proper monitoring and tracking of reservoir performance may be highly dependent on properly gathering, analyzing and weighting data relating to the reservoir. In general, leading indicators are better able to help predict future adverse events, and provide the ability to resolve or remedy such events, than lagging indicators.
The following examples are provided to illustrate how the RCAA� methodology has been used in the field to increase productivity and/or recovery of an existing reservoir. None of the reservoirs described in the examples are located in the United States, and none of the acts used to improve productivity and/or recovery were carried out in the United States. Moreover, neither the RCAA� methodology nor the underlying acts used in connection with the examples were publicly known.
EXAMPLE 1 Background Information
RCAA� Impact
EXAMPLE 2 Background Information
EXAMPLE 3 Background Information
EXAMPLE 4 Background Information
EXAMPLE 5 Background Information
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