Enhancing the Geological Models Consistency in Ensemble Based History Matching an Integrated Approach

Recently, the Ensemble Kalman Filter (EnKF) has emerged as an effective tool for performing continuous updating of petroleum reservoir simulation models. The method is firmly grounded on the theory of Kalman filters and sequential Monte Carlo techniques. The ability of the method to sequentially update the spatial properties in petroleum reservoir models, such as permeability and porosity, by integrating the dynamic production data makes it a very attractive approach. Moreover, the method takes into account the production uncertainty in the reservoir models by using error covariance matrices for the measurement vector (Production and injection rates, Gas-Oil ratio, Steam-Oil ratio, etc.) and the state vector (pressure, saturation, permeability, porosity). Similar to the traditional Kalman filter, the covariance matrices have to be tuned to reflect the uncertainty in the model and the measurements. We consider two unconventional oil reservoir models: 1) highly heterogeneous black-oil reservoir model, and 2) heterogeneous SAGD reservoir model. The results will demonstrate the advantage of using the localized EnKF for effective history matching using ensemble sizes relatively lower than what otherwise would be required with the ordinary EnKF. The results will also show the advantages of using prior knowledge available from the wells (permeability and porosity measurements) to generate initial realizations. One of the main practical advantages of history matching using the EnKF over traditional optimization based approaches is its low computational effort. The computational cost is dominated by Monte Carlo simulation of the ensemble of models only. Thus, significant computational time saving is possible by running each of the ensemble simulations on independent processors in a parallel mode. Moreover, the method can be easily integrated with any commercial reservoir simulation software.

Effective history matching of a simulation model with real data from the field requires the resolution of three outstanding issues. First, a conflict may exist between the production data and the existing geological model built from static information. Second, during model updates, geological consistency must be maintained by honoring the prior geologic information. Third, uncertainties related to mechanical and operational issues must be taken into account.;The objective of this study is to build a realistic simulation model able to predict future behavior of a complex CBM reservoir for a CO2 sequestration pilot project in Marshall County, WV by using a systematic approach for history matching. Our study is limited to building a history matched model for the Methane production.;As a general overview, the field project covered in our work includes two known coal seams, Pittsburgh coal (upper layer) and Upper Freeport coal (lower layer). Production is from six horizontal wells, two of them completed in Pittsburgh coal and the other four completed in Upper Freeport coal. This project is part of the national plan of CO2 sequestration with the goals of improving the quality of the environment and increasing methane production from coal.;In order to accomplish the objective of this study, a systematic history matching approach was devised. In this approach, data from the field are gathered and analyzed in order to be used as input in a commercial reservoir simulator software. Then, effective strategies and key reservoir parameters are defined to realistically history match the field production. The resulted approach propose a new systematic methodology for performing history matching complex reservoirs. This approach accounts for production data inconsistency related to reservoir characteristics heterogeneity, mechanical, and operational issues within different scenarios.;The systematic approach included a detail analysis of the field production data in order to learn as much about the reservoir behavior as possible. The analysis included a normalized production comparison done based on the amount of gas produced in a per-foot of the well, in a per-foot of thickness around the well, and based on the average gas content. This information will be used extensively in the history matching process, which included sensitivity analysis of conventional parameters (permeability, porosity, relative permeability, and water-gas contact), unconventional parameters (Gas content, desorption time, and Langmuir parameters), and finally wellbore parameters (skin factor, bottom-hole pressure, and well length). Using the results of the sensitivity analysis above, an analysis table is formed to be

History matching is a challenging reverse problem due to the inherited uncertainty from Earth modeling. History matching process mandates building one or more sets of representative reservoir models with minimum misfit between the reservoir models and observed field data. There are several commercial assisted history matching tools and workflows to facilitate and orchestrate this process. This paper provides performance assessment and process evaluation between two of the commonly used optimization algorithms: the Evolutionary Algorithm (EA) and the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in an automated and closed loop workflows. The workflow starts with a parametrization step to identify and quantify the static and dynamic uncertainties in the reservoir model. The objective function is then constructed to guide and measure the convergence of the optimization process. The workflow is then executed to start the model calibration and history matching process. The algorithms, EA and ES-MDA, take different routes during the optimization process. EA works by selecting the best cases (i.e. most fitting genes that resulted in a minimum misfit) in each cycle, and generates a number of simulation cases (new generation) based on the predefined gene selection criteria. The iterative ES-MDA algorithm maintains the same number of simulation cases in each iteration (as many as in the initial Ensemble) throughout the assimilation process. The EA and ES-MDA optimization algorithms have their advantages and disadvantages to the history matching process. For instance, both workflows preserve the geological consistency with the dynamic reservoir models. The automation that both workflows provide incorporates G&G (Geological and Geophysical) workflows to capture, and carryover the static uncertainty to ensure consistency throughout the history matching process. In addition, in both cases, multiple simulation models are generated to cover reservoir static and dynamic uncertainties, which are crucial to the predictive capabilities of the history matched models. Both algorithms, as known, are stochastics in nature (i.e. cannot guarantee one deterministic solution) and, therefore. the results are highly dependent on the initial ensemble. EA is highly effective in searching the solution space, it requires a large number of simulation runs to converge a solution. On the other hand, ES-MDA does not exceed the number of initial ensembles in each iteration regardless of the number of iterations. This paper presents an automated workflow that leads to multiple reliable history matched simulation models. All of the generated models are geologically consistent and capture all model uncertainties that are needed to be preserved and carried forward in prediction. Moreover, this helps in updating the models seamlessly whenever new data is available by simply re-running the workflow with the model updates.

Assisted history matching (AHM) using Design of Experiments (DoE) is one of the most commonly applied history matching techniques in the oil and gas industry. When applied properly, this stochastic method finds a representative ensemble of history-matched reservoir models for probabilistic uncertainty analysis of production forecast. Although DoE-based AHM is straightforward in concept, it can be misused in practice because the workflow involves many statistical and modeling principles that should be followed rigorously. In this paper, the entire DoE-based AHM workflow is demonstrated in a coherent and comprehensive case study that is divided in seven key stages: problem framing, sensitivity analysis, proxy building, Monte-Carlo simulation, history-match filtering, production forecast, and representative model selection. Best practices of each stage are summarized to help reservoir management (RM) engineers understand and apply this powerful workflow for reliable history matching and probabilistic production forecasting. One major difficulty in any history matching method is to define the history-match tolerance, which reflects the engineer's comfort level of calling a reservoir model "history-matched" even though the difference between simulated and observed production data is not zero. It is a compromise to the intrinsic and unavoidable imperfectness of reservoir model construction, data measurement, and proxy creation. A practical procedure is provided to help engineers define the history-match tolerance considering the model, data-measurement, and proxy errors.

Streamline-based assisted and automatic history matching techniques have shown great potential in reconciling high resolution geologic models to production data. However, a major drawback of these approaches has been incompressibility or slight compressibility assumptions that have limited applications to two-phase water-oil displacements only. We propose an approach to history matching three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. First, we utilize a generalized streamline model to account for compressible flow by introducing an 'effective density' of total fluids along streamlines. 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. We calibrate geologic models to production data by matching the water-cut and gas/oil ratio using our previously proposed generalized travel time inversion (GTTI) technique. For field applications, however, the highly non-monotonic profile of the gas/oil ratio data often presents a challenge to this technique. In this work we present a transformation of the field production data that makes it more amenable to GTTI. Further, we generalize the approach to incorporate bottom-hole flowing pressure during three-phase history matching. We examine the practical feasibility of the method using a field-scale synthetic example (SPE-9 comparative study) and a field application. Recently Ensemble Kalman Filtering (EnKF) has gained increased attention for history matching and continuous reservoir model updating using data from permanent downhole sensors. It is a sequential Monte-Carlo approach that works with an ensemble of reservoir models. Specifically, the method utilizes cross-covariances between measurements and model parameters estimated from the ensemble. For practical field applications, the ensemble size needs to be kept small for computational efficiency. However, this leads to poor approximations of the cross-covariance matrix, resulting in loss of geologic realism. Specifically, the updated parameter field tends to become scattered with a loss of connectivities of extreme values such as high permeability channels and low permeability barriers, which are of special significance during reservoir characterization. We propose a novel approach to overcome this limitation of the EnKF through a 'covariance localization' method that utilizes sensitivities that quantify the influence of model parameters on the observed data. These sensitivities are used in the EnKF to modify the cross-covariance matrix in order to reduce unwanted influences of distant observation points on model parameter updates. The key to the success of the sensitivity-based covariance-localization is its close link to the underlying physics of flow compared to a simple distance-dependent covariance function as used in the past. This flow-relevant conditioning leads to an efficient and robust approach for history matching and continuous reservoir model updating, avoiding much of the problems in traditional EnKF associated with instabilities, parameter overshoots and loss of geologic continuity. We illustrate the power and utility of our approach using both synthetic and field applications.

Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

Summary Reconciling multiscale data for reservoir characterization is a crucial issue, because different data types provide different information about the reservoir architecture and heterogeneity. It is essential that reservoir models preserve small-scale property variations observed in well logs and core measurements and capture the large-scale structure and continuity observed in global measures such as well-test and production data. In this paper, we present a new methodology to directly update fine-scale geostatistically-based reservoir models by combining gradual deformation parameterization for the fine-scale geostatistical model, and an upscaling technique for the coarse-scale flow simulation model. As consequence, both fine- and coarse-scale models are updated by dynamic data during the history matching process. The proposed methodology includes: Perturbation of the fine-scale geostatistical model using the gradual deformation parameterization. Gradual deformation ensures the preservation of the overall geostatistical properties of the fine model. Generation of the coarse-scale flow simulation model by upscaling the fine-scale geostatistical model. Fluid-flow simulation and sensitivity computation of the flow simulation results with respect to the fine-scale parameterization. This sensitivity computation is analytical and takes into account the upscaling process. Direct updating of the fine-scale geostatistical model using classical optimization process. Direct updating ensures consistency between the fine- and coarse-scale models. The accuracy of the proposed methodology was improved by calibrating the flow simulation model. The objective of this calibration is to reduce the error introduced by the upscaling step during the flow simulation. We applied successfully our methodology for fine-scale reservoir characterization process by integrating permanent downhole gauge measurements directly into a 3D-geostatistical model containing about two million gridblocks. This test is designed to highlight several key issues of the proposed methodology: Efficiency of the upscaling step, coupled with gradient-based optimization to speed up history matching. Usefulness of the calibration step for a correct integration of upscaling techniques in history matching. Capability of the methodology for maintaining consistency and coherency between fine-scale and coarse-scale models. Improvement of the reservoir characterization process by integrating dynamic data at both fine geostatistical scale and coarse simulation scale.

As the role of reservoir flow simulation increasingly impacts existing operations and field development decisions, it follows that rigor, fitness and consistency should be imposed on the calibration of reservoir flow models to dynamic data through history matching. Although a wealth of history matching techniques exist in the petroleum literature that propose novel algorithms or share case studies, seldom does the content guide the modeler in fit-for-purpose reservoir model calibration for an operating asset. To evaluate the applicability of these diverse techniques against standards required for reservoir management, an internal study was performed to benchmark four assisted history matching (AHM) techniques commonly promoted in the oil and gas industry. The techniques were vetted against a comprehensive suite of modeling requirements for multiple asset classes, integrating a variety of historical dynamic data types through the calibration of reservoir properties that control flow behavior from the field to inter-well scale. The methods benchmarked were: (1) Design of Experiments (DoE)-based, (2) Ensemble Kalman Filter and Ensemble Smoother, (3) Genetic Algorithm and (4) Generalized Travel Time Inversion. This manuscript focuses solely on the DoE-based technique.In order to consistently benchmark the techniques, a set of standards was defined against which each was evaluated to determine its suitability for widespread history matching applications. The standards involve: the capacity to parameterize (and therefore calibrate) a diversity of reservoir flow model attributes, the capacity to integrate different types of dynamic data, the level of independence from the flow simulator and the capability to provide probabilistic outcomes for predictive uncertainty assessment. Of the four techniques, the DoE-based approach uniquely satisfied all requirements. Its history matching workflow has the flexibility to incorporate any form of reservoir model parameter and to assimilate a history matching error metric for any individual or group of historical data types; therefore, benchmarking established DoE-based techniques as unambiguously the most compliant with generic asset modeling requirements. The approach was also identified as the most straightforward, both theoretically and in practical computation, and therefore applicable to the broadest range of practitioners. Perhaps most importantly, the approach demonstrated the capacity for accurate quantification of uncertainty (or non-uniqueness) in reservoir quality resulting from an exhaustive, although approximate, exploration of model parameter space and the associated history matching error metric(s).This manuscript compiles the results and insights gained from benchmarking of the DoE-based techniques through the proposal of a comprehensive assisted history matching workflow. The workflow is designed for generality while providing best practices that guide the modeler in fit-for-purpose application. Limitations of the workflow are also recognized. Key components include: selection and screening of calibration parameters based on statistical significance, development of surrogate models to characterize the relationship between parameters and the simulated historical data being integrated into the reservoir model, use of the surrogate models for exhaustive yet efficient exploration of parameter space to identify (non-unique) history matched models, and (deterministic or probabilistic) discrete reservoir model selection for use in forecast-based decision making. Each step of the AHM workflow is presented from a conceptual and applied perspective, and field applications are provided to demonstrate key concepts. Although the applications presented include two deepwater Gulf of Mexico assets, the workflow and insights provided are developed from benchmarking across a diverse suite of asset types.

Summary Accurate characterization of sub-surface oil reservoirs is an essential prerequisite to the design and implementation of enhanced oil recovery (EOR) scenarios. Specifically, in reservoir characterization, integrating static and dynamic data into reservoir models to construct accurate and realistic models has received considerable attention. Unlike most of the conventional geostatistical approaches of integrating data into reservoir models that are based on semi-variograms (two-point statistics) as a measure of spatial connectivity, a complete multiple-point (MP) statistic framework is presented in this paper. In contrast to two-point statistic methods, MP statistics-based methods are capable of reproducing curvilinear geological structures. The algorithm starts with extracting MP statistics from training images (TI) using an optimal spatial template. After collecting different patterns and building the MP histogram, the pattern reproduction process commences. This process begins from data locations and then grows to fill the whole reservoir domain. The algorithm accounts for three main practical issues: uncertainty in geological scenarios, scanning template and non-stationarity. The MP statistics algorithm (growthsim) is capable of integrating data from multiple data sources. Among these data types is dynamic data or flow history. The conventional approach to integrate production information into reservoir models is by iterative perturbation of the reservoir model until the production history of the reservoir is matched. Iterative methods have been applied till date to random fields that are completely characterized by a two-point co-variance function. In contrast, this paper presents a forward modelling approach that investigates history matching within a MP modelling framework. A novel technique implemented in this research is based on the merging of MPs inferred from history matched and geological models. Pattern growth is performed subsequently by sampling from the merged MP histograms. History matched models using the presented approach show an excellent agreement with underlying geological descriptions and match production history.

Mature and complex reservoirs are usually covered by many kinds of static and dynamic data—i.e. petrophysical, PVT, well testing, geologic, completion, and reservoir production information. This paper illustrates an effective methodology to develop a consistent simulation model, via integration of the available static and dynamic data sets, that allows better understanding of the subsurface reservoir flow characteristics. A complex gas condensate reservoir, referred to as reservoir X and first put into production 14 years ago, serves as the example in this paper. There is abundance of available data from different sources. However, the data can not be directly applied to build a reservoir prediction model because of the various measurement scales—e.g., core = about 0.1 ft long, log = tens of feet away from wellbore, and reservoir simulation = hundreds of feet for the grid size. In some cases, the same parameter is derived from different sources—i.e., permeability from core samples, logging data, and pressure transient test. Due to the extreme intrinsic complexity of reservoir X, actual subsurface flow characteristics were not well understood before this study. By using the proposed methodology, a reliable, coherent and improved reservoir simulation model was built. In the past decade, the multidisciplinary approach (usually including geology, geophysics, petrophysics, reservoir engineering, and production engineering) has gained popularity in industry's attempt to achieve more effective reservoir management. This paper focuses on a multidisciplinary approach that integrated PVT data, well tests, geologic, petrophysical, and dynamic reservoir production information to build a reservoir simulation model, which can be used to predict reservoir performance. Reservoir X, discovered in early 1980s in Tarim Basin (P.R. China), is a gas condensate reservoir without oil rim. Geologic formations of this reservoir are relatively flat with only a few degrees of inclination. The depositional environment is fluvial-delta with bedded sandstones. The southern boundary …

A persistent challenge in reservoir modeling is to assign representative values for vertical permeability in reservoir models. It is a common practice to use kv/kh directly from the core measurements but this is not at the same scale as the model grid. It is also frequent to use one value for entire zones. The approach of using a single value applied to a single zone when the reservoir is heterogeneous is not correct and the same can be said regarding using core plug kv/kh as the grid scale is larger than the core plug therefore resulting in a difference in the statistical support of the two measurements. This paper describes the technique to understand reservoir vertical connectivity by properly defining kv/kh, which later will be used as a matching parameter during the History Match process. Most of the time the data provided by the plugs is not appropriate due to the absence of vertical sampling or to problems of representativeness. Where plug data is available kv/kh ratios are often found to be close to one, a values too high to be realistic when considering the geological heterogeneity at small scale or plug scale. Even when plug measurements show homogenous at small scale (kv/kh ∼1), at grid cell scale the Kv and Kh are expected to be different (<<1). A methodology is proposed in order to give more appropriate ranges of values to the grid cell size, based on the small-scale permeability anisotrophy ratio (kv/kh) into grid cell size. A novel approach was established to get the ranges of kv/kh based on core mini-k measurements and core-plugs data. This innovative workflow also considers a multi-disciplinary approach using Experimental Design methods to better understand and validate the kv/kh ranges. The main learning from this project is the importance of understanding vertical connectivity in a complex reservoir by properly capturing heterogeneity and other major geological features in the integrated 3D dynamic model with the aim to achieve an optimized history match at reservoir level but also more importantly on a well-by-well basis.

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time‐consuming. As a result, only a small number of simulation runs are conducted and the history‐matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history‐matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history‐matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the “cheap” GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history‐matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.

Karachaganak field is one of the largest accumulations of gas-condensate in the world in production since 1985. Located in the northern Pricaspian Basin (Kazakhstan) the field is a Permo-Carboniferous isolated carbonate platform with a hydrocarbon column that resides within different environments of deposition. The distribution of reservoir properties has been largely debated because of both the depositional heterogeneity and the diagenetic overprint.These uncertainties were assessed by analyzing and integrating the vast amount of geological and production data to build a predictive history matched reservoir model.Seismic facies analysis, with support from outcrop analogues and integrated with field core and log data, reveals, within stratigraphic intervals, "Depositional Regions" (DRs) that ranges from platform interior bedded deposits to aggrading/prograding mounds, clinoforms, slopes and basin sediments. These DRs were first seismically mapped, identifying field scale heterogeneity, and then petrophysically characterized using geologic and dynamic data.A sequence of progressively optimized models was built according to geologically meaningful concepts that make use of DRs and critical petrophysical issues (such as enhanced/matrix permeability, sealing barriers and dolomitization) were in parallel addressed. A reference model has been so defined and a history match of remarkable quality has been achieved for this complex heterogeneous reservoir.The uncertainty was investigated in a pragmatic manner using HM as benchmark. The reservoir uncertainty decreases closer and closer to the wells, hence alternative models scenarios were built by gradually changing DRs petrophysical properties going away from the production wells. Using the distance and the magnitude of the perturbations as control parameters and the quality of history match as selection criterion, we could identify two "end members??. These cases represent possible alternative scenarios both consistent with the geological data and still endorsed by a high quality history match, two reservoir models capable to give a significant spread in the production forecast.

The application of elastic stress simulation for fracture modeling provides a more realistic description of fracture distribution than conventional statistical and geostatistical techniques, allowing the integration of geomechanical data and models into reservoir characterization. The geomechanical prediction of the fracture distribution accounts for the propagation of fracture caused by stress perturbation associated with faults. However, the challenge lies in estimating the past remote stress conditions which induced structural deformation and fracturing, the limited applicability of the elasticity assumption, and the latent uncertainty in the structural geometry of faults. The integration of historical production data and welltest permeability into geomechanical fracture modeling is a practical way to reduce such uncertainty. We propose to combine geostatistical algorithms for history matching with geomechanical elastic simulation model for developing an integrated yet efficient fracture modeling tool. This paper presents an integrated approach to history matching of naturally fractured reservoirs which includes 1) fracture trend prediction through elastic stress simulation, 2) geostatistical population of fracture density based on fracture trend map, 3) fracture permeability modeling integrating fracture density, matrix permeability and welltest permeability, and 4) numerical flow simulation and history matching. All of these implementations are incorporated into a single forward modeling process and iterated in the automatic history matching scheme. To obtain a history match on a reservoir model, we jointly perturb the large-scale fracture trend and local-scale geostatistical fluctuations of fracture densities rather than perturbing permeability calibrated from fractures. This strategy enables us to preserve the geological/geomechanical consistency throughout the history matching process. The geomechanically simulated fracture trend model is calibrated to both production data and geological structure map (faults and horizons) by finding the optimum remote stress condition for elastic stress-field simulation. The latter is achieved by matching the actually observed structural deformation trend with the simulated one. The smaller-scale fluctuation of fracture density is simultaneously history matched through the probability perturbation method of Caers (2003)1,2. The result of synthetic reservoir application is presented.

Reservoir simulation plays an important role in managerial decisions during the life of a reservoir. Thus, it is vital that the simulations present an accurate prospect of real reservoir performance. The main goal of the History Matching (HM) process is to improve the quality of numerical models by contrasting simulated with observed data. A typical HM process evaluates a chosen objective function (OF) comprised by non-linear functions on the uncertain reservoir properties that measure the current mismatch. Thus, the HM problem is a minimization problem where the decision variables are reservoir characterization properties and the goal is to find a set of values for the decision variables that minimize the OF. This paper focuses on the use of an optimization technique called Scatter Search (SS) to perform the HM. The main feature of SS is that it works on a set of solutions called the reference set. The goal is to improve the overall quality of the reference set after each new iteration. New solutions are generated by a non-convex combination of reference solutions. The proposed methodology was used in both automatic and assisted HM processes and the analyzed reservoir models presented increasing challenges to the algorithm. This paper describes how the HM problem was solved with the SS framework, the expected performance of the methodology in dealing with complex solution spaces and also points how it can be expanded to better scale to a growing number of optimization parameters. The application of the SS methodology to the HM problem is a recent trend. Unlike most metaheuristics, SS can be effective even when the simulation time of each instance of the problem is long. Most of the current HM methodologies do not perform well when the solution space is both large and complex.