History Matching Process Research Articles

Megacell Reservoir Simulation

Reservoir simulation has moved into a new era with the rapid advancements in parallel-computer hardware and software technologies. In the past decade, the total number of gridblocks used in a typical reservoir study increased from thousands to millions. Mega (million) -cell simulation capability allows accurate representation of reservoirs because of its ability to include detailed information provided by logs, 3D seismic, horizontal wells, and stratigraphic studies. One of the main advantages of megacell simulation is that it minimizes or eliminates upscaling effects, making more-accurate reservoir-performance predictions possible. Further more, the speed of parallel computers significantly reduces the manpower-intensive history-matching process. Megacell simulators typically are developed on parallel computers With the current low-cost parallel computers, a million-cell simulation run covering 20 years of production history takes less than 1 hour, State-of-the-art computer graphics provide an effective means of visualizing large data sets covering million-cell descriptions of a reservoir and its performance. This paper presents an overview of megacell simulation technologies in the oil industry and academia.

The Stratigraphie Method: A Structured Approach to History-Matching Complex Simulation Models

Summary This paper offers a structured approach to performing a history match on a complex, multilayered reservoir model. We recommend tools for interpreting the data and simulation results, and discuss procedures for improving the quality and efficiency of the history-matching process. This approach, the stratigraphic method (SM), was developed over 10 years on various reservoir studies. It has been used successfully on highly complex reservoirs, some containing more than 1,700 wells and over 50 years of production history. We also report on which reservoir parameters most affect matching observed-reservoir performance. This may be helpful to those with less experience working on a history-match project.

Performance Evaluation of Trilateral Wells: Field Examples

Summary Recent advances in horizontal drilling and completion technology have led to the successful completion of multilateral horizontal wells (wells with two or more laterals penetrating separate zones that produce through a single wellbore to the surface). Evaluating performance of these commingled horizontal wells poses unique problems that have led to development of a practical procedure for that puφose. This paper demonstrates a practical procedure used in evaluating the performance of three lateral (trilateral) horizontal wells. The procedure, coupling the analysis of pressure-buildup and rate/time data, involves use of a consistent mathematical model to generate pressure and rate responses for pressure-test interpretation and performance prediction. The procedure is demonstrated for several new trilateral wells drilled in the Dos Cuadras field, offshore California. Results from field tests indicate that the procedure is efficient in evaluating the performance of these wells. Reservoir and well parameters are obtained and the contribution of all laterals is confirmed from pressure-test interpretation. The computed model parameters are then used in a rate history match and accurate long-term performance predictions are made. Deconvolution is shown to improve the history-match process. In addition, the performance evaluation of these trilateral wells is compared with previously drilled single-lateral horizontal wells in the same field. The comparison confirms that the new trilateral horizontal well technology is economically viable, allowing reserves to be developed for approximately two-thirds the cost of three single-lateral horizontal wells.

Integration of Geology, Geostatistics, Well Logs and Pressure Data to Model a Heterogeneous Supergiant Field in Iran: ABSTRACT

The geological reservoir study of the supergiant Ahwaz field significantly improved the history matching process in many aspects, particularly the development of a geostatistical model which allowed a sound basis for changes and by delivering much needed accurate estimates of grid block vertical permeabilities. The geostatistical reservoir evaluation was facilitated by using the Heresim package and litho-stratigraphic zonations for the entire field. For each of the geological zones, 3-dimensional electrolithofacies and petrophysical property distributions (realizations) were treated which captured the heterogeneities which significantly affected fluid flow. However, as this level of heterogeneity was at a significantly smaller scale than the flow simulation grid blocks, a scaling up effort was needed to derive the effective flow properties of the blocks (porosity, horizontal and vertical permeability, and water saturation). The properties relating to the static reservoir description were accurately derived by using stream tube techniques developed in-house whereas, the relative permeabilities of the grid block were derived by dynamic pseudo relative permeability techniques. The prediction of vertical and lateral communication and water encroachment was facilitated by a close integration of pressure, saturation data, geostatistical modelling and sedimentological studies of the depositional environments and paleocurrents. The nature of reservoir barriers and baffles variedmore » both vertically and laterally in this heterogeneous reservoir. Maps showing differences in pressure between zones after years of production served as a guide to integrating the static geological studies to the dynamic behaviour of each of the 16 reservoir zones. The use of deep wells being drilled to a deeper reservoir provided data to better understand the sweep efficiency and the continuity of barriers and baffles.« less

Sand Body Geometry and Permeability Areal Distribution in Fluvial Sandstones Reservoir: ABSTRACT

This paper describes as the integration between geological and reservoir engineering activities led to a more accurate definition of permeability areal distribution in a giant oil field where depositional environment retains a fundamental control on reservoir petrophysical characteristics. The data source was: well-log and well-test data of 60 wells, laboratory analyses and sedimentological studies of about 10 fully cored wells, more than 20 years of field performance. The sedimentological study pointed out the presence of three non-marine environments: low-sinuosity braided channel, overbank splay and flood basin. The areal distribution of these environments was controlled by the synsedimentary tectonic movement. Permeability/porosity correlations were investigated on the basis of core data and the continuous permeability profile was estimated. Based on these data the arithmetic, geometric and harmonic average were calculated to investigate the flow characteristics of the individual reservoir units by comparing the log permeability with the well-test permeability assumed as the reference value. The results of this comparison led to a further analysis of the sedimentological and electrofacies data. The following conclusions were drawn: the areas characterized by the arithmetic average correspond to over bank splay environment where low permeable sandstones with high lateral continuity sedimented; the areas characterized by themore » geometric and harmonic average correspond to channel environment where more permeable sandstones with poor lateral continuity sedimented. The permeability maps, optimized according to these sedimentological concepts, gave a fundamental improvement to the history match process.« less

Field Development Options for a Waterflooded Heavy-Oil Reservoir

Summary Battrum Unit 4 is a moderately heavy-oil reservoir in Saskatchewan producing under waterflood from a thin sand. This paper describes a producing under waterflood from a thin sand. This paper describes a history match of previous field behavior and systematically analyzes through the use of numerical simulation the potential benefits to production of further waterflooding (with and without infill production of further waterflooding (with and without infilldrilling), steamflooding, and horizontal drilling. It is found that the remaining oil recovery potential of a steamflood with horizontal wells is significantly higher than that of any of the waterflood options. Introduction This reservoir study concerns oil recovery options for a waterflooded, moderately heavy-oil reservoir. the Battrum Northeast pool, located 45 km northwest of Swift Current, Sask. Various pool, located 45 km northwest of Swift Current, Sask. Various field development plans were examined through numerical simulation and screening for a suitable EOR method. Steamflooding was the EOR technique selected, although the reservoir conditions were somewhat borderline for application of this process. Use of horizontal wells also was studied. Reservoir Characteristics and Geology The pool, discovered in 1955, started to produce under natural depletion in Aug. 1963. A waterflood was initiated in 1966 to arrest declining pressures. The field was later unitized and divided into four units. Mobil Oil Canada Ltd. operates Units 1 through 3, which are being fireflooded; Sceptre Resources Ltd. operates Unit 4, the subject of this study. BP Resources Canada Ltd. (now Talisman Energy Inc.) has a 25 % interest in Unit 4. The original pattern used for the waterflood was an inverted nine-spot drifted on a 32-ha spacing. In1979, when water cuts became excessive, infill wells were drilled on a 16-haspacing to improve sweep efficiency. Further infill drilling was carried out in1983. Fluid migration across the boundary between Unit 3 and the more easterly Unit 4 was thought to have been controlled by water injection at the boundary. Oil production peaked initially in 1970 and subsequently in 1985, with an apparent rapid decline underway at the time the study began. The pay zone in Unit 4 consists of the Roseray (main producing zone) and Success sands. Fig. 1shows the isopachs for the Roseray, which communicates with the Success over a good portion of the reservoir. The well pattern can also be seen. Unit 4 has no gas cap. Table 1 lists the main characteristics of the reservoir rock and its fluids. The reservoir varies considerably in quality, especially in the more productive Roseray sand. Table 2 gives some of the variations in Roseray properties. These variations are included automatically in the history-matching process through the usual specification of reservoir data for the field. They also play a direct role in steamflood modeling when decisions must be made on where to locate a pattern element and what local reservoir properties to use for the pattern model. properties to use for the pattern model. Note that field wide fracturing was carried out at Unit 4 for two reasons: to improve productivity from the tighter sand and to prevent plugging of the near-well formation and sand screens by prevent plugging of the near-well formation and sand screens by fines. While the fracturing program only marginally improved productivity from the tighter sand, its effect on preventing sand productivity from the tighter sand, its effect on preventing sand plugging was much more beneficial. plugging was much more beneficial. Preliminary Reservoir Screening Preliminary Reservoir Screening Battrum Unit 4 was screened carefully for applicability of various EOR methods. Miscible processes (hydrocarbon or CO2)were rejected because the required pressures would have exceeded fracture pressures. Immiscible CO2 might have been successful, but there pressures. Immiscible CO2 might have been successful, but there was no economical CO2source in the area. Alkaline and surfactant processes were discarded because of their complexity and their processes were discarded because of their complexity and their generally mixed field performance. Polymer flooding was rejected because the foreseeable benefits were few; sweep has been good in this reservoir. Polymer diversion similarly was judged to be of little benefit because of existing crossflow between the two formations. In-situ combustion had been in operation in the neighboring Unit 3 and had not yet proved its effectiveness. Laboratory and reservoir studies indicated that Unit 4 may be a candidate for steamflooding, considering the advanced state of technology available for operating steamfloods and completing thermal wells. This investigation was intended partly to provide additional information on the viability of steamflooding Unit 4. Study Objectives The main objective of this study was to determine the best way to operate Battrum Unit 4. A history match of previous production was carried out first to determine present conditions and reservoir parameters. parameters. P. 888

Multilevel grid method for history matching multi-dimensional multi-phase reservoir models

The multilevel grid method (different from the multigrid method commonly used for accelerating the iterative solution process of linear algebraic equations) is used here to develop an efficient numerical algorithm for history matching multi-phase multi-dimensional reservoir models. The purpose of history matching is to identify the unknown oil reservoir structural parameters by matching the computed pressure values with measured pressure values at observation wells. One of the major problems in history matching is that the identified parameter distribution is very sensitive to the initial guess used to start the history matching process. The problem becomes worse as the number of grid points increases. With the multilevel grid, the history matching process starts on a coarse grid; the parameter distribution identified on the coarse grid is then interpolated onto a finer grid and used as a better initial guess to start the history matching on the finer grid. Although the history matching process becomes more sensitive to the initial guess as the number of grid points increases, the better initial guess obtained from the solution on the coarse grid offsets the increased sensitivity and improves the quality of the identified parameter distribution on the finer grid. The grid can be refined repeatedly until the desired resolution has been achieved. Numerical examples for two- and three-dimensional reservoir models are given in the paper, which demonstrate that the algorithm presented here is able to identify the reservoir absolute permeability distributions varying over the field by an order of magnitude with a constant initial guess deviating from the true distribution by two orders of magnitude. The use of the multilevel grid also reduces the computational complexity of the algorithm and speeds up the computations.

Confidence Limits on the Parameters and Predictions of Slightly Compressible, Single-Phase Reservoirs

Abstract Methods of nonlinear regression theory were applied to the reservoir history-matching problem to determine the effect of erroneous problem to determine the effect of erroneous parameter estimates obtained from well testing parameter estimates obtained from well testing on the future prediction of reservoir pressures. Two examples were studied: well testing in a radial one-dimensional slightly compressible reservoir and in an undersaturated, two-dimensional, heterogeneous oil field. The reservoir parameters of permeability, porosity, external radius, and pore volume were considered, and the effects of pore volume were considered, and the effects of measurement error, test time, and flow rate on the confidence limits were computed. Introduction The operation of a reservoir simulator requires accurate estimates of the reservoir properties. However, the simulation parameters, such as permeability, porosity, and reservoir geometry, are permeability, porosity, and reservoir geometry, are usually unknown unless coring and physical property analysis have been undertaken. Because of the cost of these procedures, it is more desirable to use the pressures measured at the well during a well test pressures measured at the well during a well test and indirectly compute the important parameters of the system. By using history matching of the test data to obtain the system parameters, the future pressure behavior of the reservoir can be predicted pressure behavior of the reservoir can be predictedSeveral studies on history matching have indicated that the welltest approach for determining the reservoir parameters often suffers from incorrect and nonunique parameter estimates. The factors that affect the parameter estimation can be classified as model errors, observability, measurement errors or noise, history time, test procedure, and optimization procedure. Model errors arise from the inaccuracy of the model and the numerical integration. For example, a reservoir simulator is only a reasonable approximation for flow through porous media. Solution of a model equation by numerical means also introduces roundoff and discretization errors. Observability of the system plays an important role in estimating the reservoir parameters. Depending on the location of the well and the number of data points, it may not be possible to determine uniquely all reservoir parameters from the measurements made at that well. Observability is strictly a function of the reservoir model used. At a given well, pressure measurements may only reflect the values of the parameters in specific zones of the reservoir. If a specific zone away from the well does not affect the measured pressure, then the system is not observable at that particular location. A rigorous definition of observability can be found in other papers. Measurement errors in the pressures and flow rates are another source of unrealistic parameter estimates. Longer history times always give more information about the reservoir as long as the system remains in a dynamic state. The nature of the system input (well flow rate) also affects the accuracy of the estimates and predictions. The final source of incorrect parameter estimates arises because the history-matching problem, posed mathematically, is usually a nonlinear programming problem that must be solved computationally. Such problem that must be solved computationally. Such a problem yields multiple extrema that often can lead to a relative minimum (rather than a global minimum) in the numerical search for the smallest matching error. Also, the magnitude of the objective function can be quite insensitive to the parameters selected, thus causing the optimization procedure to terminate prematurely. The above factors control the history-matching process; with actual data, it is usually impossible process; with actual data, it is usually impossible to identify the exact contributions of each factor to the errors in the parameter estimates. Since a certain amount of error will be introduced into the estimated parameters from the history-matching process, it is parameters from the history-matching process, it is useful to study the magnitude of this error resulting from various sources under controlled simulation conditions. Also, it is important to determine how the errors in the parameters are reflected in the future predictions of the pressures. SPEJ P. 42

History Matching Three-Dimensional Models Using Optical Control Theory

Abstract History matching three-dimensional reservoir simulator models by manually changing parameters is a difficult job. In this paper, the authors extend their previously published(Soc. Pet. Eng., Jour., Vol. 14, No.6, p. 593, and Vol. 15, No.4, p. 347) automatic history matching method to handle three-dimensional models. Special attention is given to matching historical pressures which are not single cell pressures, but instead are either wellbore pressuresor volumetric averages of a vertical column of cells. Using these measurements, estimates of vertical conductance and well productivity indices can now be made in addition to horizontal(x and y direction) conductances and cell storage values. A by-product of the matching procedure is on estimate of "flow split" – how much of a well's total flow goes to each faller. Case studies are shown to illustrate productivity index estimation using ellbore pressures and to illustrate three-dimensional conductance and storage estimation using volumetric average-cell column pressures. Introduction An important phase of any simulation of reservoir behaviour is history matching. Once the most uncertainreservoir parameters have been estimated by matching calculated and observed performance, the model can be used for predictions. For many years, researchers have sought means for automating the history matching process. The need for automation became more urgent as models grew in complexity, making matching, by manual adjustment of parameters, difficult, if not impossible. Early approaches(1) involved dividing the reservoir into zones of "uniform" properties and applying some optimization technique to estimate the zonal parameters. The number of zones that could be used was limited, because of computer time limitations. Thus, one had to arbitrarily zonate the reservoir and in the process possibility introduce serious modeling error. Recently, two papers have appeared(2, 3) which represent significant advances in automatic history matching, In these papers, the authors used optimal-control theory to derive the gradient of the measure (a sum of squares) of the deviation between calculated and observed pressures. This gradient is a vector of firstrtial derivatives, of the deviation measure, with respect to the uncertain parameters. Once known, the gradient can be used in a simple optimization scheme (steepest descent) to give improved parameter values which decrease the deviation measure. The authors of this paper subsequently showed (4) how the optimal control theory-matching procedure can be coupled with a two-dimensional multiphase reservoir simulator and illustrated the effectiveness of this procedure on several field examples. This paper further extends the work in Reference 4. Specifically, we focus on matching three-dimensional multiphase reservoir simulator models. To achieve our goal, we develop methods of matching historical pressures which may be either flowing well-bore values or volumetric average pressures of a vertical column of cells. In addition, we modify our algorithm to treat well "flow-splitting", because we generally know only a well's total flow rate and must somehow determine he amount of fluid going to each layer. Two illustrations are included. The first is an actual history match of the Kaybob Beaverhill Lake A Pool.