Shale gas currently provides 20% of domestic supply, is targeted by half of the gas-directed drilling rigs, and represents the large majority of domestic resources. However, modern shale plays, their development strategies and their engineering analysis are young by comparison to those of conventional reservoirs. Uncertainty in shale gas reserves has significant implications at both the micro and macro levels.Conventional reservoir engineering tools must be viewed as potentially inadequate (or even inappropriate) for the evaluation of shale gas performance primarily because of the extremely low aggregate permeability of these systems, but also because of other unique aspects of the systems. Reservoir modeling (simulation) has an important role as an assessment and prediction tool; however, the character of the reservoir (induced and enhanced natural fractures) must be considered, as well as the geological and fluid characteristics. Rate-transient analysis (modern decline analysis) techniques are also more rigorous and have been expanded and adapted to fit the uniqueness of shale gas production. Application of each method for shale gas is discussed, including methods and limitations. These two techniques more closely represent the physics of shale gas production, but their implementation is often prohibitive.By way of necessity, much engineering evaluation is performed using Arps decline curve analysis. This technique is argued by some to be inappropriate due to a lack of theoretical support and demonstrated tendency to over-estimate reserves in tight gas systems. Given the limitations, practical methods exist to reduce error associated with its use. A newer decline method, power-law exponential, is also investigated.
Any attic oil project tries to displace with gas the oil located above the structurally highest well in the reservoir. Results from a numerical simulator show that the most important controllable variable in attic oil recovery is the volume of gas injected during each cycle. A method is presented for calculating the required gas injection volume for each cycle presented for calculating the required gas injection volume for each cycle of an attic oil project. Introduction Oil recovery from high-relief reservoirs can be increased by downstructure gas injection. In this process, often called "attic oil recovery," gas is injected in the structurally highest well in the reservoir. The injected gas will migrate upstructure, forming a secondary gas cap and displacing oil downward.In high-relief reservoirs, which typically occur around "piercement type" salt domes, field development is complicated by the lack of control on the upper limits of the structure. To ensure a commercial completion, operators usually locate development wells a safe distance from the estimated sand/salt interface. In reservoirs with an active bottomwater drive, much of the updip oil will not have been produced when the highest well in the structure waters out. Sidetracking or drilling new wells is expensive and risky. It would be almost impossible to locate new wells to drain all updip oil adequately. Reservoirs with steep dip and high permeability allow injected gas to migrate to the most permeability allow injected gas to migrate to the most inaccessible areas of the pinchout, providing good lateral coverage. Injected gas may be traded economically for oil as long as the price of a unit volume of reservoir oil is sufficiently greater than the price of a corresponding unit volume of reservoir gas.Many authors have reported on field applications of the attic oil recovery process. In 1971, Combs and Knezek published theoretical guidelines and field data concerning the maximum gas/oil segregation rate and the minimum and actual gas requirements to recover 1 bbl oil.This paper presents results of a numerical model study of an attic oil recovery process and defines the variables that control the reservoir performance of a successful project. A method is presented to calculate the required project. A method is presented to calculate the required gas injection volumes. Numerical Model The numerical model used in this study is a conventional beta model that was modified to simulate the attic oil recovery process properly. The model includes these features: three-phase flow of oil, water, and gas; gas/oil solubility; gas/oil and oil/water capillary pressure; three-phase relative permeability with hysteresis effects; variable bubble-point scheme; variable flow rates; gravity effects, both areally and vertically; conformance to any reservoir geometry; and variable rock and fluid properties. properties. Even with the advent of relatively efficient three-dimensional reservoir simulators, computing costs for a meaningful study can become expensive. A two-dimensional areal model can simulate the same problem, provided the distribution and flow of fluids in the vertical dimension is included implicitly in the areal model. Several authors' have reported on the theory and calculation techniques of "pseudofunctions." Jacks et al. presented a technique for calculating the "dynamic pseudofunctions" that could be applied over a wide range of How rates and initial fluid saturations. JPT P. 1323
Coupling Probabilisitic Methods and Finite Difference Simulation: Three Case Histories Dwayne C. Purvis, SPE, Cawley, Gillespie and Associates, Inc. (CG&A); Richard F. Strickland, SPE, CG&A; Richard A. Alexander, SPE, CG&A; and M. Anthony Quinn, SPE, CG&A Abstract Advances in computing power have made possible the combination of probabilistic methods with the conventional practice of finite difference simulation. This paper presents and demonstrates a methodology for probabilistic finite difference simulation to determine and examine a range of potential production rate profiles and ultimate recoveries by simulating hundreds of scenarios. Due to the high degree of uncertainty in the three case histories discussed, finite difference simulation coupled with probabilistic methods offered the best tool to predict production profiles given a wide variety of assumptions about reservoir character and producing conditions. A Monte Carlo simulation was run in each case to create several hundred possible combinations of the uncertain variables. These variables became inputs to the same number of finite difference simulations. The resulting forecasts were ranked and P10, P50 and P90 values were determined depending on the needs of those using the forecasts. When probabilistic simulation is used to predict a range of results, care should be taken when combining the forecasts to properly honor their history-dependent nature. Discounting techniques can be applied to the forecasts before ranking in order to make them useful for economic decisions. In the one case in which a comparison was made, it was also observed that, depending on the skewness of the input distributions, the median forecast was similar to the deterministic, "most likely" forecast. Introduction Monte Carlo analysis has been frequently applied in the petroleum industry as it permits a quantitative analysis of the associated risks. In traditional Monte Carlo applications the calculations are rather simple, such as a summation to obtain total cost or multiplication to obtain oil-in-place. Distributions of the calculated values are analyzed to ascertain the total and combined uncertainty implied by the input values. Advances in computing power have made it feasible to extend Monte Carlo techniques to complex calculations such as finite difference simulation. The coupling of these powerful tools allows the modeler to quantify the uncertainty in the generated rate forecasts. In the three examples presented, the uncertainty in the production forecast was particularly important to the development decision, making them good candidates for a probabilistic approach. All three studies involved large uncertainties in reservoir parameters due to limited information. A single simulation forecast was not deemed adequate because it could not describe the associated uncertainty. In each case, an appropriate finite difference model was created. Probability distributions were developed for the major variables that controlled the production profile and ultimate recovery. The most important parameters controlling the production profile varied between the cases. Among the variables considered were porosity, water saturation, net thickness, gas-water contact, permeability and areal extent. Monte Carlo techniques were used to create several hundred combinations of uncertain reservoir parameters. Each combination was then simulated producing a unique production profile. The resulting, simulation-based production forecasts can be interpreted for technical risk. Such characterization of technical risk is becoming critically important to the increasingly sophisticated strategies used in the petroleum industry. Statement of Theory and Definitions Traditional Monte Carlo applications address uncertainty of data inputs to simple calculations such as oil-in-place. Finite difference simulation, on the other hand, has been used to assess uncertainty on a larger scale, such as that between qualitatively different reservoir characterizations. P. 289
A three dimensional conditional simulation model, integrating a comprehensive data base coupled with engineering, geological, and petrological studies, has significantly enhanced reservoir management capabilities in the Schneider (Buda) Field. Presented are the results of a series of conditional simulations of the Schneider (Buda) Field. The Schneider (Buda) Field is developed in a Buda reef complex. Porosity types change significantly between reservoir facies. A three dimensional conditional simulation generated using only well control pints did not adequately characterize the reservoir. An intermediate two dimensional conditional simulation using additional assumptions was then generated to refine and control the final three dimensiondl conditional simulation. The resulting three dimensional conditional simulation delineates permeability distributions that better: 1) represent the calculated heterogeneity, 2) match the available core data, and 3) model the geologic characterization, including the defined facies. The resulting distributions have been applied to a three dimensional fluid flow simulation to better predict fluid movement through the reservoir.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractMany papers have been written concerning the energy industry's need to become more efficient in its methodologies and operations. The shrinking qualified workforce and global economic factors are placing further pressures on the energy sector to streamline processes. This requirement extends itself to all phases of the energy industry, especially with multidisciplinary multi-organizational teams. 1-5
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