Efficient Reservoir Management Research Articles

GIS –Based Morphometric Analysis of Two Reservoir Catchments of Arkavati River, Ramanagaram District, Karnataka

In countries like India, where the population pressure is continuously increasing, land and water resources are limited and their wide utilization is imperative. Drainage basins, catchments and sub catchments are the fundamental units for administrative purposes to conserve natural resources. The catchment management concept recognizes the interrelationships among the linkages between uplands, low lands, land use, geomorphology, slope and soil. Soil and water conservation are the key issues in reservoir catchment management while demarcating catchments. Detailed morphometric analysis of a basin is of great help in understanding the influence of drainage morphometry on landforms and their characteristics. One of the advantages of quantitative analysis is that many of the basin parameters derived are in the form of ratios (dimensionless), thus providing an effective comparison irrespective of the scale.Morphometric analysis of the reservoir basins is considered to be the most satisfactory method because it enables in understanding of the relationship of various aspects within a drainage basin, a comparative evaluation to be made of different drainage basins developed in different geomorphological and topographical conditions or situations. Detailed morphometric analysis was carried out using ARC- GIS for Manchanabele reservoir catchment and Nellegudde reservoir catchment of Arkavati river system. Arkavati River is the tributary of river Cauvery which covers a catchment area of 4038 sqkm. Manchanabele Reservoir and Nelligudde Reservoir is constructed across river Arkavati. Manchanabele Reservoir has an independent catchment of 152.99sq km and Nelligudde reservoir has an independent catchment of 66.64 sq km. The results of the morphometric analysis reveal that Manchanabele catchment is less elongated with high erosion and peak flow. It has a strong relief and steep ground slope. Nelligudde reservoir catchment is elongated with low soil erosion and medium peak flow. It has an average steep slope with medium relief. This study in conjunction with high resolution satellite data coupled with a geographic information system (GIS) will help to better understand the status of land forms and their process, soil erosion, drainage management and ground potential conditions for efficient reservoir catchment planning and management.

Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach

Efficient reservoir management requires the implementation of generalized optimal operating policies that manage storage volumes and releases while optimizing a single objective or multiple objectives. Reservoir operating rules stipulate the actions that should be taken under the current state of the system. This study develops a set of piecewise linear operating rule curves for water supply and hydropower reservoirs, employing an imperialist competitive algorithm in a parameterization–simulation–optimization approach. The adaptive penalty method is used for constraint handling and proved to work efficiently in the proposed scheme. Its performance is tested deriving an operation rule for the Dez reservoir in Iran. The proposed modelling scheme converged to near-optimal solutions efficiently in the case examples. It was shown that the proposed optimum piecewise linear rule may perform quite well in reservoir operation optimization as the operating period extends from very short to fairly long periods.

Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi R׳Mel gas field, Algeria

Characterization of shaly sand reservoirs by well log data is a usual way of describing oil/gas field reservoirs. Over the last few years, several studies have been conducted in the field of petroleum engineering by applying artificial intelligence. This work represents a petrophysical-based method that uses well logs and core plug data to predict well log data recorded at depth in a shaly sand reservoir of Triassic Formation in Hassi R׳Mel field, Algeria. In the study of oil reservoirs, the prediction of absolute permeability is a fundamental key in reservoir descriptions and has a direct impact, in particular, on effective completion designs, successful water injection programs and more efficient reservoir management. The Triassic Formations of Hassi R׳Mel fields are composed of sandstones and shaly sands with dolomites. Logs from 10 wells are the starting point for the reservoir characterization. This paper presents a hybrid neuro-fuzzy model based on the use of data from four wells regarding porosity and permeability estimation. A fuzzy logic approach is used to calibrate the calculated permeability and core permeability; and a neural network was developed in this model, based on the data available from the field. Fuzzy analysis is based on fuzzy logic and is used to choose the best well logs with regard to core porosity and permeability data. A neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core measurements. Porosity and permeability are predicted in these wells through linear regression; and back-propagation models are constructed and their reliabilities are compared according to the regression coefficients for predictions in un-cored sections. This investigative hybrid neuro-fuzzy method becomes a powerful tool for the estimation of reservoir properties from well logs in oil and natural gas development projects.

Consideration of Voidage-Replacement Ratio in Well-Placement Optimization

Summary Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, the voidage-replacement ratio (VRR) and net present value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field-development activities on the environment, whereas NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well-placement optimization has been performed mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of unity (or close to unity) during waterflooding. A multiobjective approach incorporating NPV and VRR is proposed for solving the well-placement-optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase had the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights was used in the third phase to describe the relative importance of the NPV and the VRR, and a comparison of how these weights affect the optimized NPV and VRR values is provided. We applied the method to determine the optimum placement of wells to three sample reservoirs: the first with a distributed permeability field, the second being a channel reservoir with four facies, and the third being a slightly heterogeneous reservoir. Two evolutionary-type algorithms—the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE)—were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary-oil-recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdictions with a requirement for meeting a VRR of unity.

Implementing i-field-Integrated Solutions for Reservoir Management: A San Joaquin Valley Case Study

Summary To achieve more-efficient business processes by exploiting data and knowledge management, real-time systems, and advanced analytical tools, most major integrated and service companies have developed, or are in the process of developing, intelligent programs also known as "Digital Oil Field of the Future" (CERA 2002). The i-field™ program represents Chevron's efforts to integrate people, processes, technology, and information to achieve its vision of upstream transformation. Ultimately, this vision translates to higher-quality decision making and changes the method in which assets are operated. A new multiyear i-field™ program, Integrated Reservoir Management (i-RM), was initiated in the San Joaquin Valley (SJV) to enable efficient reservoir management (RM) and quality decision making for heavy-oil assests operating under steamflood enhanced-oil-recovery (EOR) technologies. The paper describes the journey undertaken by the project team to build a proprietary steamflood-integrated RM tool that answers the basic never-ending engineering requirements: instant access to clean data, single source of information for everyone, and seamless data-tools linkage. The ultimate benefit is not only increased time spent by engineers and earth scientists to analyze data, but also consistent project design workflows and decision-making processes across fields. In addition to the previously described documented benefits, the manuscript includes the operator's experience with project framing, development, deployment, and user acceptance.

Seasonal changes and spatial variation in the water quality of a eutrophic tropical reservoir determined by the inflowing river

The water quality of a eutrophic tropical reservoir was studied over a 5-year period (2000–2004), with quarterly sampling (Jan, Apr, Jul, and Oct) carried out at 3 sampling stations with one station in each of 3 zones (fluvial, transitional, and lacustrine). During the wet season, large amounts of phosphorus were introduced into the reservoir by the increased inflow of the river. Dissolved oxygen, pH, and chlorophyll a levels peaked in the wet season; pH, chlorophyll a, and total phosphorus in several cases were recorded above the recommended Brazilian guidelines. Dissolved oxygen was lower than acceptable levels in the euphotic layer and reached very low levels in the hypolimnion, indicating thermal stratification. Efficient reservoir management is necessary to restore environmental quality, and our results indicate that selective withdrawal may be an effective means of improving the quality of water in Funil Reservoir.

Novel Analytical Methods of Temperature Interpretation in Horizontal Wells

Summary Long (horizontal) completion intervals typically show a wide variation in the inflow distribution along their length because of either formation heterogeneity or (frictional) flow pressure losses. Monitoring of the inflow profiles in such wells is an important step in efficient reservoir management. Accurate temperature measurements [using distributed temperature sensors (DTSs), permanent downhole gauges (PDGs), or other forms of production logging] have become more widely available in recent years. Many published papers describe temperature sensing and its phenomenological interpretation, but few attempts have been made recently to develop a comprehensive mathematical basis for the analysis of downhole temperature behavior. This paper presents a holistic, analytical, mathematical model for calculation of the temperature profile in horizontal wells producing liquids for reservoirs where thermal recovery methods are not being employed. The model presented in this paper rigorously accounts for (1) the Joule-Thomson (JT) effect, (2) convection, (3) transient fluid expansion, and (4) time-dependent heat loss to the surrounding layers. A synthetic horizontal-well model has been built using a commercial, scientific simulator as a test bed to provide the data to allow evaluation of the efficacy of our novel analytical methods. Asymptotic analytical solutions have also been found for transient and steady-state flow. It has also been found possible, in addition to these constant-flow-rate solutions, to apply the well-known pressure-analysis solution techniques for the estimation of (1) thermal properties and (2) inflow profiling. The methods proposed here can be applied to a wide variety of well completion types, flow conditions, and system properties. These methods form the basis for the calculation of oil-and water-flow phase cuts and distributions that are based purely on temperature measurements. Their use will further increase the potential applications of the modern downhole monitoring and control capabilities currently being installed in wells.

Technology Focus: History Matching and Forecasting (April 2011)

Technology Focus History matching and forecasting of geologically complex reservoirs are challenging aspects for efficient reservoir management. The foremost reason is the high level of uncertainty that exists in the reservoir models because of the limited, sparse, and multiscaled reservoir data available. The problem is further heightened for deepwater provinces such as the Gulf of Mexico, Brazil, and West Africa. The objective of the history matching is to adjust the geological model and its parameters (e.g., permeabilities and porosities) such that the simulation model is able to reproduce the well-flow-rate and pressure histories reasonably. The result of the history-matching process is a new simulation model that can be very different from the original geological model. However, this history matching is an inverse problem, meaning that there is no unique solution. Different arrangements of reservoir parameters can furnish many simulation models tuned to the available past data. Even though each matched reservoir model is capable of reproducing the observed data, these various geological models can generate different production forecasts. The greatest challenge is obtaining multiple efficient history-matched models for realistic uncertainty estimation. Conventional history matching is a trial-and-error process. The mismatch between observed and simulated values is minimized by adjusting reservoir parameters over successive simulation runs. Even for experienced reservoir engineers, the process is very time consuming and, in general, a single adjusted scenario can be geologically inconsistent. On the other hand, modern history-matching techniques apply numerical optimization and generate multiple geologically consistent adjusted scenarios. Most of the history-matching research work being carried out today can be classified into four types of algorithms: gradient-based methods, stochastic methods, data-assimilation approaches, and streamline techniques. Data-assimilation techniques, such as an ensemble Kalman filter, represent a very active research area, and new and potentially promising methodologies have been developed. The papers summarized in this feature and those in the additional-reading list provide good examples of new methodologies. History Matching and Forecasting additional reading available at OnePetro: www.onepetro.org SPE 129183 “A New Adaptively Scaled Production-Data-Integration Approach Using the Discrete Cosine Parameterization” by Eric Bhark, SPE, Texas A&M University, et al. SPE 131627 “History Matching With Sensitivity-Based Parameter Modifications at Gridblock Level” by H. Almuallim, SPE, FirmSoft Technologies, et al. SPE 141216 “History Matching a Field Case Using the Ensemble Kalman Filter With Covariance Localization” by Alexandre A. Emerick, SPE, Petrobras, et al.

Fiber-Optic-Enabled Coiled Tubing Used To Access and Stimulate a Trilateral Oil Producer

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 126730, ’First Worldwide Application of Fiber Optic Enabled Coiled Tubing With Multilateral Tool for Accessing and Stimulating a Trilateral Oil Producer, Saudia Arabia,’ by Ahmed Al-Zain, SPE, Rifat Said, SPE, Salman Gamber, and Saad Al-Driweesh, SPE, Saudi Aramco, and Khzam Al-Shahrani, SPE, and Jan Jacobsen, Schlumberger, originally prepared for the 2009 Kuwait International Petroleum Conference and Exhibition, Kuwait City, Kuwait, 14-16 December. Recent advances in coiled-tubing (CT) technology enable rigless interventions of complex wells while acquiring real-time downhole measurements. The fiber-optic-enabled CT (FOCT) provides combined services of data acquisition and running other CT tools required for well treatments. The fiber-optic telemetry system consists of a pump-through bottomhole assembly (BHA) with a casing-collar locator (CCL), pressure and temperature sensors, optical fibers inside CT, and a surface readout unit. Introduction Horizontal wells have become more commonplace in many Saudi Aramco fields. Recent advances in drilling and completion practices enable more-complex wells with various types and shapes, such as extended-reach wells, bilaterals, multilaterals, and maximum-reservoir-contact (MRC) wells, to be drilled. Benefits of drilling these wells include reduction in development and operating cost because of the smaller number of wells and improvement in reservoir performance and management. These complex wells are drilled for various reasons and many objectives, which may include but not be limited to increased production, enhanced reservoir characterization, improved sweep, maximized recovery, and to ensure efficient reservoir management. Background Well-A was drilled and completed as a trilateral openhole oil producer in 2008. The well was cased with 95/8-in. casing to the top of the producing reservoir at 6,220 ft measured depth (MD). An 8½-in. hole then was drilled directionally 3,000 ft inside the reservoir. A 7-in. liner was run and cemented at 9,220 ft MD. The main bore (L-0) then was drilled successfully to a total depth (TD) of 13,040 ft MD. After that, Lateral-1 (L-1) was drilled, after opening a casing window at 7,987 ft MD, to 11,350 ft MD. Following that, Lateral-2 (L-2) was drilled from a casing window at 6,422 ft MD to 9,786 ft MD. Figure 1 in the full-length paper shows trajectories of the laterals. The well was completed with 4½×3½×27/8-in. tubing and equipped with an electrical submersible pump (ESP) to produce the well. The ESP assembly has a bypass (Y-tool), that allows normal well interventions and other well surveys. The minimum inside diameter (ID) is 2.441 in. downhole in the tail pipe. After completion, the well was flowed for cleanup by the ESP, but it was found unable to flow, indicating severe formation damage.

Control of Algal Scum Using Top-Down Biomanipulation Approaches and Ecosystem Health Assessments for Efficient Reservoir Management

The objectives of this study were algal control and health assessments in a temperate eutrophic reservoir. Laboratory and mesocosm-scale in situ top-down biomanipulation experiments using planktivorous fishes and filter-feeding macroinvertebrates were conducted along with identification of the limiting nutrient using nutrient enrichment bioassays (NEBs), and ecosystem health evaluation based on the modified index of biological integrity model (Reservoir Ecosystem Health Assessment; REHA). Nutrients and N/P ratio analyses during 5 years revealed that the reservoir was in a eutrophic–hypertrophic state and that the key limiting nutrients, based on the NEBs, varied among seasons. Reservoir trophic guilds indicated declines in sensitive and endemic fish species and dominance of tolerant omnivores. Model values from multimetric REHAs averaged 25.8, indicating that the ecological health was in “fair to poor” condition. Overall microcosm biomanipulation tests suggested that macroinvertebrates, specifically Palaemon paucidens and Caridina denticulata, were effective candidates for phytoplankton control, compared to fishes. In situ mesocosm experiments revealed the highest removal rates with bluegreen algae and a phytoplankton size fraction of 2–19 μm (Re > 90%, Mann–Whitney U = 64.5–74.0, p < 0.01), the dominant fractions in the reservoir. Our biomanipulation technique may provide a key tool for efficient management and restoration of eutrophied reservoirs.

Reservoir-Fluid Sampling and Characterization — Key to Efficient Reservoir Management

This paper, SPE 103501, is based on paper 101517 presented at the 2006 Abu Dhabi International Petroleum Exhibition &amp; Conference, Abu Dhabi, 5-8 November. Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Abstract Reservoir-fluid properties play a key role in the design and optimization of injection/production strategies and surface facilities for efficient reservoir management. Inaccurate fluid characterization often leads to high uncertainties in in-place-volume estimates and recovery predictions, and hence affects asset value. Reservoir-fluid pressure/volume/temperature (PVT) characterization begins with acquisition of adequate volumes of representative fluid samples followed by PVT-data measurement with strict quality-assurance/quality-control (QA/QC) protocols and phase-behavior modeling through best-practice methods. In this paper, key steps involved in accurate fluid characterization are discussed for a wide spectrum of fluid types ranging from extra-heavy oils to highly volatile near-critical fluids and lean gas condensates undergoing a wide range of production processes from simple depletion to complex tertiary recovery. Selection of appropriate sampling methods and tools, design of tool strings, and customizing procedures are demonstrated through these examples. Routine and special laboratory-fluid-analysis strategies for various fluid types and for different production strategies are highlighted. Fluid-modeling techniques including optimum-component selection, accurate C7+ characterization, robust Gibb's energy minimization, and gravity/chemical equilibrium calculations are demonstrated through appropriate field examples. Introduction Reservoir-fluid PVT properties are critical for efficient reservoir management throughout the life of the reservoir, from discovery to abandonment (See complementary paper by Honarpour et al. 2006.) Reliable PVT properties of in-situ fluids are essential for the determination of in-place volumes and recovery-factor calculations and are key input to reservoir simulations for technical evaluation of reservoir-development/-depletion plans. Fluid characterization and distribution within the reservoir help in defining reservoir continuity and communication among various zones. Interpretation of well-test data and the design of surface facilities and processing plants require accurate fluid information and its variation with time. In addition to initial reservoir-fluid samples, periodic sampling is necessary for reservoir surveillance. Reservoir-fluid characterization consists of several key steps:acquisition of representative samples,identification of reliable service laboratories to perform PVT measurements,implementation of QA/QC procedures to ensure data quality, anddevelopment of mathematical models to capture fluid-property changes accurately as functions of pressure, temperature, and composition. The fluid type and production processes dictate the type and the volume of required fluid data. This paper outlines recommended sampling techniques, PVT-data-acquisition strategies, and modeling methods and presents field examples covering a wide range of fluid types from heavy oils to lean gas condensates and production processes such as depletion, pressure maintenance, and miscible recovery.

Reservoir-Fluid Sampling and Characterization — Key to Efficient Reservoir Management

Reservoir-Fluid Sampling and Characterization — Key to Efficient Reservoir Management

Efficient real-time reservoir management using adjoint-based optimal control and model updating

The key ingredients to successful real-time reservoir management, also known as a “closed-loop” approach, include efficient optimization and model-updating (history-matching) algorithms, as well as techniques for efficient uncertainty propagation. This work discusses a simplified implementation of the closed-loop approach that combines efficient optimal control and model-updating algorithms for real-time production optimization. An adjoint model is applied to provide gradients of the objective function with respect to the well controls; these gradients are then used with standard optimization algorithms to determine optimum well settings. To enable efficient history matching, Bayesian inversion theory is used in combination with an optimal representation of the unknown parameter field in terms of a Karhunen–Loeve expansion. This representation allows for the direct application of adjoint techniques for the history match while assuring that the two-point geostatistics of the reservoir description are maintained. The benefits and efficiency of the overall closed-loop approach are demonstrated through real-time optimizations of net present value (NPV) for synthetic reservoirs under waterflood subject to production constraints and uncertain reservoir description. For two example cases, the closed-loop optimization methodology is shown to provide a substantial improvement in NPV over the base case, and the results are seen to be quite close to those obtained when the reservoir description is known a priori.

Efficient Closed-Loop Production Optimization Under Uncertainty

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 94241, “Efficient Closed-Loop Production Optimization Under Uncertainty,” by P. Sarma, L.J. Durlofsky, and K. Aziz, Stanford U., prepared for the 2005 SPE Europec/EAGE Annual Conference, Madrid, Spain, 13–16 June. This closed-loop approach for real-time production optimization consists of three key elements—adjoint models for efficient parameter and control-gradient calculation, polynomial chaos expansions for efficient uncertainty propagation, and Karhunen-Loeve (K-L) expansions and Bayesian inversion theory for efficient real-time model updating (i.e., history matching). The closed-loop procedure provides a substantial improvement in net present value (NPV) over the base case, and the results seem to be very close to those obtained when the reservoir description is known a priori. Introduction A primary goal of reservoir modeling and management is to enable decisions that maximize the production potential of the reservoir. Real-time model-based reservoir management, also known as the closed-loop approach, has generated significant interest. This method uses model-based optimization of reservoir performance under geological uncertainty while incorporating dynamic information in real time, which acts to reduce model uncertainty. For such schemes to be practical, several algorithmic advances are required. The full-length paper details algorithms for efficient closed-loop reservoir management that are based on the application of optimal-control theory, Bayesian inversion theory, K-L expansions, and polynomial chaos expansions. Closed-Loop Approach Fig. 1 shows the “System” box that represents the real system over which some cost function (e.g., NPV or cumulative oil produced) is to be optimized for real-time model-based reservoir management. The system consists of the reservoir, wells, and surface facilities. Here, a set of controls [well rates and bottomhole pressures (BHPs)] are used to maximize or minimize NPV or cumulative oil produced. The low-order-model box represents the approximate model of the system, which, in this case, is the simulation model of the reservoir and facilities. Because knowledge of the reservoir generally is uncertain, the simulation model and its output also are uncertain. The basic idea of the closed-loop approach is to perform an optimization step on the approximate model, apply the obtained optimal controls on the real reservoir, gather new data as they become available, and assimilate these data to reduce model uncertainty. This loop can be repeated many times over the life of the reservoir. The closed-loop approach for efficient real-time optimization consists of three key components: efficient optimization algorithms, efficient model-updating algorithms, and efficient algorithms for uncertainty propagation. In this work, an adjoint model is applied for the efficient calculation of gradients of the objective function with respect to the controls, which then are used by gradient-based optimization algorithms, such as sequential quadratic programming, for optimization. For the model-updating procedure, Bayesian inversion theory is used, and an efficient parameterization of the uncertain parameter field is applied using the K-L expansion. The K-L expansion essentially transforms the correlated input random field into a much smaller set of independent random variables, thereby enabling the use of standard gradient-based inversion algorithms and associated adjoints for very efficient model updating while maintaining the geological correlation.

Inferring the statistical distribution of velocity heterogeneities by statistical traveltime tomography

Understanding the internal heterogeneities of reservoirs is one of the key issues in better recovery and efficient reservoir management. Seismic data are widely used to map subsurface heterogeneities. These heterogeneities can include variations in wave velocity and rock density, which can be used to interpret variations in reservoir properties such as porosity, lithofacies, and fluids. This paper describes a statistical tomography method to infer the spatial statistics of subsurface velocity heterogeneities from seismic data. We consider an acoustic wave propagating in a medium represented as a single macromodel superimposed on statistically stationary random velocity perturbations. While the macromodel is retrieved by classical seismic methods, the picked traveltimes and their fluctuations are used to estimate the covariance function of the spatially varying velocity perturbations. We present a formulation based on ray‐theoretical results and describe two algorithms: one using the prestack traveltimes and the other using the stacking velocities. The methods are tested with synthetic seismic reflection data in an idealized medium (with a Gaussian spatial covariance) and with synthetic transmission data in a more geologically realistic medium. Then, the two algorithms are applied on real data. The estimates of the spatial statistics obtained from inverting the traveltime statistics match reasonably well with the true parameters of the heterogeneous media.

Integrated Reservoir Characterization Process Model: a planning and scheduling tool for E&amp;P organization

The ultimate goal of an Exploration and Production (E&amp;P) organization is to increase reserves and optimize production in a cost-effective manner within the current Health, Safety and Environment regulations. Efficient reservoir management requires in-depth knowledge of reservoir properties and their distribution within the field. Saudi Aramco&apos;s Exploration Organization formed a multidisciplinary team in order to develop an Integrated Reservoir Characterization Process Model (IRCPM).

Crude Oil Prorationing As An Optimization Problem

Abstract Criteria employed in various jurisdictions to prorate oil production from a number of reservoirs to market demand are expressed as linear and quadratic programming problems. Prorationing in practice uses rather cumbersome approximate methods. Introduction Crude oil prorationing to market demand has largely been confined to the North American continent. It deals with the problems which arise when three circumstances occur in combination.The rule of capture (if you produce it you own it, even if itfound its way to your well from an adjoining property).Fragmented reservoir ownership and operation.Accessible markets not sufficient to absorb output at full capacity. This combination if unchecked can result in competitive extraction, with each operator trying to drain hydrocarbons from adjoining properties. Where some producers own refineries and some do not, the latter may be severely drained if they cannot find markets and cannot get their more fortunate competitors to share their markets. On other continents a different land tenure system prevails, the rule of capture generally does not apply, and reservoirs are typically under the control of a single operator. If the first two conditions hold and markets are capable of absorbing output at full capacity, the problem of allocating output among pools disappears. There remains the problem of achieving efficient reservoir management while ensuring that output within a reservoir is in accordance with some criterion of fairness to the ownership interests, thereby addressing the problem of competitive extraction. This paper expresses the full range of oil prorationing criteria that have come to the writer's attention as linear or quadratic programming problems, and should provide a basis for analogous treatment of any that may have been overlooked. Prorationing in Alberta The Energy Resources Conservation Board (ERCB) has administered Alberta's prorationing system since the program's province-wide introduction in 1950 (although the Turner Valley field became subject to prorationing in 1938 after years of legal challenges), a program that has seen a number of major changes over the years. In the late 1960s Alberta's prorationing system changed fundamentally. Proratable demand, which had been allocated among pools according to productive capacity, shifted to proratable reserves (defined as the simple average of initial recoverable reserves and remaining recoverable reserves) as a basis for allocation; at the same time a new approach was adopted by reservoir engineers to establish each pool's maximum authorized production rate (maximum rate limitation or MRL in Alberta parlance). The allocation of proratable demand was subject to both a maximum and a minimum production level for each pool under prorationing. This prorationing process follows four steps:Elicit from oil buyers a forecast ("nomination") of how much Alberta oil each will purchase in the forthcoming month and total them to obtain market demand.Subtract from market demand that portion of oil production not subject to prorationing (synthetic crude, pentanes plus, heavy crude, etc.) and adjust for foreseeable inventory changes to obtain proratable demand.