Manual History Matching Research Articles

Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data

Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usually based solely on the production data with no systematic link to the geological and geophysical data, and therefore, the obtained permeability distribution may be not geologically consistent. Several other reparameterization techniques have attempted to preserve geological consistency by incorporating 4D seismic data; however, these techniques cannot be applied to our fractured basement reservoirs (FBRs) as they do not have 4D seismic data. Taking into account these challenges, in this study, an AHM methodology and workflow have been developed using a new reparameterization technique. This approach attempts to minimize the potential for geological nonconsistency of the calibrated results by linking the permeability to geophysical data. The proposed methodology can be applied to fields with only traditional geophysical data (3D seismic and conventional well logs). In the proposed workflow, the spatial distributions of seismic attributes and geomechanical properties were calculated and estimated from 3D seismic data and well logs, respectively. After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. Then, the calibration of the permeability distribution is performed by adjustment of the ANN model. Modification of the ANN model is performed using the simultaneous perturbation stochastic approximation (SPSA) algorithm to calibrate transmission coefficients in the ANN model to minimize the discrepancy between the simulated results and observed data. The developed methodology is applied to calibrate the permeability distribution of a simulation model of Bach Ho FBR in Vietnam. The effectiveness of the methodology is evident by comparing the historical matches with an available manually history-matched simulation model. The application shows that the proposed methodology could be considered as a suitable practical approach for adjusting the permeability distribution for FBR reservoir simulation models.

Seismic characterization of CO2 storage driven by time-lapse images of a prior injection using the artificial neural network

To optimize geologic CO2 storage and ensure its safety, it is necessary to demonstrate conformance between the reservoir simulations and geophysical monitoring such as time-lapse (TL) seismic. This process, known as history matching, often relies on subjective judgment and intuition of a reservoir modeling team because a direct examination of the multitude of plausible geologic scenarios is prohibitively expensive. The artificial neural network (ANN) aims to reconstruct the observed plume based on a set of seismic attribute maps. Via a randomized test, the trained model then provides an estimate of the importance of each attribute according to the attribute’s contribution to the accuracy of the plume prediction. This same test is also used to identify specific geologic controls for each part of the CO2 plume. The developed ANN is then used to forecast a CO2 plume that will likely arise from a future injection into the same formation 700 m away from the previous injection. The predicted map of the probability of the occurrence of CO2 after the future injection looks reasonable and agrees with existing reservoir simulations. At the same time, the neural network predicts some potential risks (e.g., across the fault migration) that were not considered in the fluid flow simulations. Although the neural network cannot fully replace high-fidelity fluid flow simulations, it can highlight geologic and petrophysical scenarios that should be simulated. Hence, our workflow may significantly improve the efficiency and accuracy of manual history matching.

Уточнение значений проницаемости при адаптации гидродинамической модели

Reservoir simulation models are used to design oil field developments, estimate efficiency of geological and engineering operations and perform prediction calculations of long-term development performances. A method has been developed to adjust the permeability cube values during reservoir model history-matching subject to the corederived dependence between rock petrophysical properties. The method was implemented using an example of the Bobrikovian formation (terrigenous reservoir) deposit of a field in the Solikamskian depression. A statistical analysis of the Bobrikovian formation porosity and permeability properties was conducted following the well logging results interpretation and reservoir modelling data. We analysed differences between the initial permeability obtained after upscaling the geological model and permeability obtained after the reservoir model history-matching. The analysis revealed divergences between the statistical characteristics of the permeability values based on the well logging data interpretation and the reservoir model, as well as substantial differences between the adjusted and initial permeability cubes. It was established that the initial permeability was significantly modified by manual adjustments in the process of history-matching. Extreme permeability values were defined and corrected based on the core-derived petrophysical dependence KPR = f(KP) , subject to ranges of porosity and permeability ratios. By using the modified permeability cube, calculations were performed to reproduce the formation production history. According to the calculation results, we achieved convergence with the actual data, while deviations were in line with the accuracy requirements to the model history-matching. Thus, this method of the permeability cube adjustment following the manual history-matching will save from the gross overestimation or underestimation of permeability in reservoir model cells.

A practical and efficient iterative history matching workflow for shale gas well coupling multiple objective functions, multiple proxy-based MCMC and EDFM

A manual history matching is a time-consuming process and too risky for an investment decision because it cannot capture the uncertainty of subsurface information and production forecast. Therefore, Assisted History Matching (AHM) was adopted to obtain multiple reservoir realizations for the probabilistic production forecast. Markov Chain Monte Carlo (MCMC) is one of the AHM in the Bayesian statistics that have benefits of quantifying uncertainty without bias, not being trapped in any local minima. However, the AHM in shale reservoirs has not been widely studied due to the unconventional shale's complexities such as fracture modeling. The continuum approach cannot accurately model the connection between hydraulic fractures and matrix blocks in shale reservoirs. While, Discrete Fracture Modeling (DFM) by using local grid refinement can overcome the issue of the continuum approach, there is still a limitation of computational efficiency. Therefore, Embedded Discrete Fracture Modeling (EDFM) has been developed to combine the benefits of dual-continuum model and DFM. While DFM is an intrusive technique to model fractures, EDFM can account the mass transfer between matrix cells and fractures without being intrusive to the model. Besides, EDFM provides more efficient computational time than DFM. In this study, we proposed a practical and efficient iterative AHM workflow by integrating the benefits of using multiple objective functions, multiple proxy-based MCMC algorithm, EDFM and commercial reservoir simulator. Then, we demonstrated an application of the proposed workflow to a real shale gas well by matching both gas and water rates. The field application shows that the proposed AHM workflow efficiently finds the history matching solutions of 64 out of 300 simulation runs (21%) with a total computational time of one day. Furthermore, the workflow captures the uncertainty of reservoir and fracture parameters in terms of posterior distribution. Also, we performed the probabilistic production forecast and obtained P10-50-90 estimated ultimate recovery of gas production.

Assisted history matching using artificial neural network based global optimization method – Applications to Brugge field and a fractured Iranian reservoir

Abstract Reservoir simulation is a powerful predictive tool used in reservoir management. Constructing a simulation model involves subsurface uncertainties which can greatly affect prediction results. Quantifying such uncertainties for a field under development necessitates history matching that is a difficult inverse problem with non-unique solutions. History matching is used to minimize the difference between the observed field data and the simulation results and requires numerous simulation runs. In many engineering simulation-based optimization problems, the number of function evaluations is a prohibitive factor limited by time or cost. History matching in hydrocarbon reservoir simulation is one of such computationally expensive problems which pose challenges in the field of global optimization. One way to overcome this difficulty is to use an artificial neural network (ANN) as a surrogate model. This article presents an ANN-based global optimization method that is used for history matching problem. The method has been applied to an Iranian fractured oil reservoir and the famous Brugge field benchmark. Computational results confirm the success of this method in history matching. We compare history matching results obtained by the proposed method with those of manual history matching and those obtained by simulation based direct optimization algorithm. The results compares favourably with manual history matching in terms of matching quality. The proposed method is superior than the simulation based direct optimization algorithm in finding multiple matched scenarios in less computation time.

History Matching the Norne Full-Field Model Using an Iterative Ensemble Smoother

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 164902, ’History Matching of the Norne Full-Field Model Using an Iterative Ensemble Smoother,’ by Yan Chen, SPE, International Research Institute of Stavanger, and Dean S. Oliver, SPE, Uni Center for Integrated Petroleum Research, prepared for the 2013 EAGE Annual Conference and Exhibition/SPE Europec, London, 10-13 June. The paper has been peer reviewed and is scheduled for publication in the SPE Reservoir Evaluation & Engineering journal. This paper describes the application of the iterative ensemble smoother to the history matching of the Norne field in the North Sea, which has a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of identification of important variables, generation of an initial ensemble, plausibility of results, and efficiency of minimization. Introduction The Norne field produces from an oil and gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and nine injectors in the field. Water-alternating- gas injection is used as the depletion strategy. Production rates of oil, gas, and water of the 22 producers from 1997 to 2006 and repeat formation tester (RFT) pressure from 14 wells are available for model calibration. The full-field simulation model has 22 layers, each with dimension of 46×112 cells. The total number of active cell is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother [Levenberg- Marquardt ensemble randomized maximum likelihood (LM-EnRML)] is used for history matching the Norne full-field model using production data and RFT pressure. The model parameters that are updated include permeability, porosity, and net-to-gross ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from the LM-EnRML. The LM-EnRML is able to achieve improved data match compared with the manually history matched model after three iterations. Updates from the LM-EnRML do not introduce artifacts in the property fields as are produced in the manually history matched model. The automated workflow is also much less labor intensive than manual history matching. Ensemble-based methods use information from an ensemble of reservoir models to compute directions of change to the model. The most popular of these methods, the ensemble Kalman filter (EnKF), has been successfully applied on synthetic test cases in which well-bywell data match required many degrees of freedom.

History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother

Summary Although ensemble-based data-assimilation methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother have been extensively used for the history matching of synthetic models, the number of applications of ensemble-based methods for history matching of field cases is extremely limited. In most of the published field cases in which the ensemble-based methods were used, the number of wells and the types of data to be matched were relatively small. As a result, it may not be clear to practitioners how a real history-matching study would be accomplished with ensemble-based methods. In this paper, we describe the application of the iterative ensemble smoother to the history matching of the Norne field, a North Sea field, with a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of the identification of important variables, the generation of an initial ensemble, the plausibility of results, and the efficiency of minimization. We also discuss the challenges encountered in the use of the ensemble-based method for complex-field case studies that are not typically encountered in synthetic cases. The Norne field produces from an oil-and-gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and 9 injectors in the field. Water-alternating-gas injection is used as the depletion strategy. Production rates of oil, gas, and water of 22 producers from 1997 to 2006 and repeat-formation-tester (RFT) pressure from 14 different wells are available for model calibration. The full-field simulation model has 22 layers, each with a dimension of 46 × 112 cells. The total number of active cells is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother (LM-EnRML) is used for history matching. The model parameters that are updated include permeability, porosity, and net-to-gross (ntg) ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four different reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from LM-EnRML. LM-EnRML is able to achieve improved data match compared with the manually history-matched model after three iterations. Updates from LM-EnRML do not introduce artifacts in the property fields as in the manually history-matched model. The automated workflow is also much less labor-intensive than that for manual history matching.

History matching and production prediction of a horizontal coalbed methane well

This paper presents a numerical simulation study that demonstrates history matching and production prediction for an actual horizontal coalbed methane (CBM) well located in Australia. A brief analysis of limited core analysis and well log data is presented. Numerical reservoir simulation is used to carry out a manual history matching to the field data of gas and water production rates and well bottomhole pressure. The matching parameters are porosity, relative permeability and skin factor. The reported field data show that there are sharp changes in the well bottomhole pressure and water and gas rates. This response of the reservoir is matched with a numerical model that has a varying skin factor along the horizontal well. This is deemed reasonable given that the drilling fluid has longer contact with the formation at the heel of the well, causing more formation damage. But the field data indicates that the formation damage is mitigated quickly with production. This is explained by the fact that the invaded mud is forced back during water and gas production and coal shrinkage. The production predictions show that skin factor and coal shrinkage have important effects on the CBM production of a horizontal well. However, the coal formation damage controls the gas rate more than the shrinkage for the examined case study with the assumed coal shrinkage parameters.

A Multiscale Workflow for History Matching in Structured and Unstructured Grid Geometries

SummaryWe present the development and field application of a workflow for multiscale reservoir-model calibration that seamlessly integrates production data into the reservoir description from the facies to the grid-cell scale. To start with, the permeability field is parameterized using a novel grid-connectivity-based transformation basis that can be applied with any model geometry, including unstructured and corner-point grids. The parameterization basis functions emerge from spectral decomposition of the grid-connectivity Laplacian and are related to the structural harmonics of the grid. To reconcile data with model resolution during history matching, we first use the coarsest-scale basis functions to identify the large-scale variability. Additional smaller-scale basis elements are then adaptively incorporated to successively refine the model to a level supported by data resolution. During refinement, the inclusion of more detailed basis functions into the parameterization is determined by generic modal frequency when the prior model is unavailable or by using prior information when available. In the final step of the workflow, a streamline-based inversion is performed to locally adjust the reservoir model at grid-cell resolution along preferential-flow paths defined during the coarser-scale parameterization.We demonstrate the suitability and effectiveness of the developed workflow through application to an offshore turbidite reservoir with frequent well intervention, including shut-ins and recompletions. The static model has over 300,000 cells, a complex channelized interpretation with faults, four injector/producer pairs with deviated wells, and over eight years of production history, including water cut and pressure data. The grid-connectivity-based parameterization effectively updates the prior regional permeability at scales and in locations warranted by the data, while preserving the geologic continuity and avoiding ad hoc redefinition of regions given the sparse well pattern. The multiscale calibrated-permeability field indicates flow communication previously unrecognized in static geologic interpretation or manual history matching.

An Automatic History Matching Method of Reservoir Numerical Simulation Based on Improved Genetic Algorithm

Reservoir history matching provides an effective way to accurately understand petrophysical characteristics and evaluate reservoir development potential. At present, history matching methods mainly include manual work and automatic history matching. Compared with manual history matching, automatic history matching can not only avoid randomness and blindness caused by researchers, but also reduce the workload significantly. In this paper, an automatic history matching method of reservoir numerical simulation based on improved genetic algorithm was proposed, the validity of which was proved by taking the SZ 36-1 typical reservoir of Bohai oilfield for example. Results show that the method proposed in this paper is effective for reservoir history matching problems due to its strong reliability and fast convergence speed.

A hierarchical streamline-assisted history matching approach with global and local parameter updates

The effective strategies for traditional manual history matching commonly follow a structured approach with a sequence of adjustments from global to regional parameters followed by local changes in model properties, associated with matching for pressure (reservoir energy), flood front progression, and individual well performance. In contrast, many of the automatic history matching methods utilize parameter sensitivities or gradients to directly update the fine-scale reservoir properties, potentially combining elements at all of these scales. In this paper we present a hierarchical streamline-assisted history matching approach that emulates the traditional structured procedures. First, a probabilistic approach is used to understand the uncertainty in the large-scale static and dynamic parameters, and to calibrate these global parameters. In this global calibration, the intent is to develop multiple models that all match the field performance. This global calibration is followed by a streamline sensitivity-based deterministic model calibration for local permeability changes in which each of the distinct models created in the global match are history matched in additional detail.In the probabilistic global calibration, design of experiments and response surface methodologies with evolutionary algorithms are used to calibrate the global parameters. Typical parameters are regional pore volume multipliers, regional vertical and areal transmissibility multipliers, fault transmissibilities and aquifer strength. Key global parameters are first identified via a sensitivity analysis and an initial ensemble of models that span these parameters is created. The cases studied in this sensitivity analysis are used to construct a proxy model using experimental design and response surface analysis. An improved genetic algorithm with heat-bath sampling is used to generate an updated ensemble of models conditioned to static MDT pressures and total liquid rates at the wells, corresponding to a traditional pressure history match. Next, each ensemble member is updated using water-cut, GOR and flowing BHP via sensitivity-based local permeability calibration. We utilize streamline-derived analytic sensitivities to determine the spatial distribution and magnitude of these local changes.The proposed approach was tested by a 3D synthetic case and a field application. Our hierarchical approach appears to be stable in the sense that the local changes for permeability (inner loop) do not invalidate the pressure history match (outer loop). This is not entirely unexpected since the local permeability changes are regularized by a norm constraint that minimizes deviations from the globally updated model. Nonetheless, this is a very useful property in that it allows us to powerfully combine history matching procedures on global and local scales.

A dual scale approach to production data integration into high resolution geologic models

Inverse problems associated with reservoir characterization are typically under-determined and often have difficulties associated with stability and convergence of the solution. A common approach to address this issue is through the introduction of prior norm constraints, smoothness regularization or reparameterization to reduce the number of estimated parameters. We propose a dual scale approach to production data integration that relies on a combination of coarse-scale and fine-scale inversions while preserving the essential features of the geologic model. To begin with, we sequentially coarsen the fine-scale geological model by grouping layers in such a way that the heterogeneity measure of an appropriately defined ‘static’ property is minimized within the layers and maximized between the layers. Our coarsening algorithm results in a non-uniform coarsening of the geologic model with minimal loss of heterogeneity and the ‘optimal’ number of layers is determined based on a bias-variance trade-off criterion. The coarse-scale model is then updated using production data via a generalized travel time inversion. The coarse-scale inversion proceeds much faster compared to a direct fine-scale inversion because of the significantly reduced parameter space. Furthermore, the iterative minimization is much more effective because at the larger scales there are fewer local minima and those tend to be farther apart. At the end of the coarse-scale inversion, a fine-scale inversion may be carried out, if needed. This constitutes the outer iteration in the overall algorithm. The fine-scale inversion is carried out only if the data misfit is deemed to be unsatisfactory. We demonstrate our approach using both synthetic and field examples. The field example involves waterflood history matching of a structurally complex and faulted offshore turbiditic oil reservoir. Permeability and fault transmissibilities are the main uncertainties. The geologic model consists of more than 800,000 cells and 10 years of production data from 8 producing wells. Using our dual scale approach, we are able to obtain a satisfactory history match with a finite-difference model in less than a day in a PC. Compared to a manual history matching, the dual scale approach is shown to better preserve the geological features and the pay/non-pay juxtapositions in the original geologic model.

Structured Approach for Probabilistic Assisted History Matching Using Evolutionary Algorithms: Tengiz Field

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 116212, "A Structured Approach for Probabilistic Assisted History Matching Using Evolutionary Algorithms: Tengiz Field Applications," by H. Cheng, SPE, and K. Dehghani, SPE, Chevron Energy Technology Company, and T. Billiter, Tengizchevroil Kazakhstan, prepared for the 2008 SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21-24 September. The paper has not been peer reviewed. Model calibration, or history matching, commonly has been conducted on a single deterministic model with a trial-and-error approach. Advances have been made in experimental design for use of numerical models in probabilistic forecasting. By use of evolutionary algorithms, a structured (staged) assisted history-matching method can be applied by considering probabilistic ranges of relevant input parameters and tailored objective functions for each stage of the process. A workflow was designed so that the ranges of selected parameters used to attain the best solutions at each stage could be carried over to the next stage to continue the history-match process. Introduction Numerical models are used to predict a range of ultimate recoveries and to appraise different development scenarios for oil and gas assets. The validity of reservoir models for performance prediction in brownfield assets is determined by their calibration against the historical production data. History Match. Model calibration has been conducted by changing regional and local reservoir properties to reconcile the model with observed production data. In manual history matching, a structured approach is used widely in which the sequence of scales of adjustments has been from global to flow units (regional), followed by local changes in model properties. In a geologically based automatic history matching, integrating production data typically requires minimization of a predefined data misfit and penalty terms to match the observed and calculated production response. There are two general methods to assist history matching by derivative-free (or direct search) technologies. In one, optimization of the objective function relies mainly on proxies. In this approach, experimental design is used to generate a response surface to guide the history-matching process. The second method uses genetic algorithm (GA) and evolution strategy. In this application, proxies and evolutionary algorithms were used together to accelerate the history-match process. Workflow Issues. Probabilistic history matching by use of proxies from experimental design and evolutionary algorithms, combined with parallel computing, brought about new tools for achieving a range of possibilities for acceptable history-match solutions. The structured-approach workflow of manual history matching has not been applied in probabilistic history-matching techniques. The assisted-history-matching process using experimental design and stochastic methods can be conducted by adjusting all the parameters at the same time regardless of considering any hierarchy in scale.

Adaptive Multiscale Streamline Simulation and Inversion for High-Resolution Geomodels

Summary A particularly efficient reservoir simulator can be obtained by combining a recent multiscale mixed finite-element flow solver with a streamline method for computing fluid transport. This multiscale-streamline method has shown to be a promising approach for fast flow simulations on high-resolution geologic models with multimillion grid cells. The multiscale method solves the pressure equation on a coarse grid while preserving important fine-scale details in the velocity field. Fine-scale heterogeneity is accounted for through a set of generalized, heterogeneous basis functions that are computed numerically by solving local flow problems. When included in the coarse-grid equations, the basis functions ensure that the global equations are consistent with the local properties of the underlying differential operators. The multiscale method offers a substantial gain in computation speed, without significant loss of accuracy, when basis functions are updated infrequently throughout a dynamic simulation. In this paper, we propose to combine the multiscale-streamline method with a recent "generalized travel-time inversion" method to derive a fast and robust method for history matching high-resolution geocellular models. A key point in the new method is the use of sensitivities that are calculated analytically along streamlines with little computational overhead. The sensitivities are used in the travel-time inversion formulation to give a robust quasilinear method that typically converges in a few iterations and generally avoids much of the time-consuming trial-and-error seen in manual history matching. Moreover, the sensitivities are used to enforce basis functions to be adaptively updated only in areas with relatively large sensitivity to the production response. The sensitivity-based adaptive approach allows us to selectively update only a fraction of the total number of basis functions, which gives substantial savings in computation time for the forward flow simulations. We demonstrate the power and utility of our approach using a simple 2D model and a highly detailed 3D geomodel. The 3D simulation model consists of more than 1,000,000 cells with 69 producing wells. Using our proposed approach, history matching over a period of 7 years is accomplished in less than 20 minutes on an ordinary workstation PC.

Compressible Streamlines and Three-Phase History Matching

Summary Reconciling high-resolution geologic models to field production history is still by far the most time-consuming aspect of the workflow for both geoscientists and engineers. Recently, streamline-based assisted and automatic history-matching techniques have shown great potential in this regard, and several field applications have demonstrated the feasibility of the approach. However, most of these applications have been limited to two-phase water/oil flow under incompressible or slightly compressible conditions. We propose an approach to history matching three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. First, we use a generalized streamline model to account for compressible flow by introducing an "effective density" of total fluids along streamlines. This density term rigorously captures changes in fluid volumes with pressure and is easily traced along streamlines. A density-dependent source term in the saturation equation further accounts for the pressure effects during saturation calculations along streamlines. Our approach preserves the 1D nature of the saturation equation and all the associated advantages of the streamline approach with only minor modifications to existing streamline models. Second, we analytically compute parameter sensitivities that define the relationship between the reservoir properties and the production response, viz. water-cut and gas/oil ratio (GOR). These sensitivities are an integral part of history matching, and streamline models permit efficient computation of these sensitivities through a single flow simulation. Finally, for history matching, we use a generalized travel-time inversion that has been shown to be robust because of its quasilinear properties and converges in only a few iterations. The approach is very fast and avoids much of the subjective judgment and time-consuming trial-and-error inherent in manual history matching. We demonstrate the power and utility of our approach using both synthetic and field-scale examples. The synthetic case is used to validate our method. It entails the joint integration of water cut and gas/oil ratios (GORs) from a nine-spot pattern in reconstructing a reference permeability field. The field-scale example is a modified version of the ninth SPE comparative study and consists of 25 producers, 1 injector, and aquifer influx. Starting with a prior geologic model, we integrate water-cut and GOR history by the generalized travel-time inversion. Our approach is very fast and preserves the geologic continuity.

Fast History Matching of Finite-Difference Models Using Streamline-Derived Sensitivities

Summary We propose a novel approach to history matching finite-difference models that combines the advantages of streamline models with the versatility of finite-difference simulation. Current streamline models are limited in their ability to incorporate complex physical processes and cross-streamline mechanisms in a computationally efficient manner. A unique feature of streamline models is their ability to analytically compute the sensitivity of the production data with respect to reservoir parameters using a single flow simulation. These sensitivities define the relationship between changes in production response because of small changes in reservoir parameters and, thus, form the basis for many history-matching algorithms. In our approach, we use the streamline-derived sensitivities to facilitate history matching during finite-difference simulation. First, the velocity field from the finite-difference model is used to compute streamline trajectories, time of flight, and parameter sensitivities. The sensitivities are then used in an inversion algorithm to update the reservoir model during finite-difference simulation. The use of a finite-difference model allows us to account for detailed process physics and compressibility effects. Although the streamline-derived sensitivities are only approximate, they do not seem to noticeably impact the quality of the match or the efficiency of the approach. For history matching, we use a generalized travel-time inversion (GTTI) that is shown to be robust because of its quasilinear properties and that converges in only a few iterations. The approach is very fast and avoids many of the subjective judgments and time-consuming trial-and-error steps associated with manual history matching. We demonstrate the power and utility of our approach with a synthetic example and two field examples. The first one is from a CO2 pilot area in the Goldsmith San Andreas Unit (GSAU), a dolomite formation in west Texas with more than 20 years of waterflood production history. The second example is from a Middle Eastern reservoir and involves history matching a multimillion-cell geologic model with 16 injectors and 70 producers. The final model preserved all of the prior geologic constraints while matching 30 years of production history.

Field-Scale Characterization of Permeability and Saturation Distribution Using Partitioning Tracer Tests: The Ranger Field, Texas

Summary This paper discusses the application of an efficient streamline-based inversion method to a large multiwell multitracer Partitioning Interwell Tracer Test (PITT) in the McClesky sandstone, the Ranger field, Texas, to characterize both permeability and oil saturation distribution. During a typical PITT, a conservative and a partitioning tracer are injected into the reservoir. The partitioning tracer gets partially absorbed into the oil phase, leading to a separation in the tracer responses that can be used to infer oil saturation distribution in the tracer-swept area. Our approach is extremely efficient because it relies on analytic computation of the sensitivity of the tracer response to reservoir parameters such as permeability and saturation using a single streamline simulation. We follow a two-step procedure whereby we first match the conservative tracer response to determine the permeability distribution, and then match the partitioning tracer response to obtain oil saturation distribution in the reservoir. The entire history matching took less than 6 hours on a PC as opposed to several months typically required for a manual history matching. We compared our results to a manual history match obtained using a finite-difference simulator. Both the manual history matching and the streamline-based inversion identified similar large-scale trends in permeability and saturation distribution. However, well-specific matches were significantly improved over those obtained through the manual history matching. Our approach is much more efficient in terms of computation time and effort, and the results are less sensitive to personal bias compared to manual history matching. Finally, we discuss a procedure to assess the results in terms of resolution of the estimates of permeability and saturation distribution.