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

Ganna field is a part of North Bahariya concession which is located in Abu El-Gharadig basin, Western Desert, Egypt. The field was discovered and started production in 2004 through the well Ganna-1 from Middle Abu Roash “G” reservoir. Five main reservoirs were tested and produced during the field life namely Abu Roash “E”, Upper Abu Roash “G”, Middle Abu Roash “G”, Lower Abu Roash “G”, and Upper Bahariya. A 3D reservoir simulation study was performed to aid in field future development. Major challenges appeared during history matching process. The main challenge was the commingled production wells producing from multiple reservoirs with different characteristics. Even most of the static pressure points were measured commingle as well. Post-production RFT data was the key utilized to calibrate the history match and reach the most accurate reservoir characterization for each reservoir individually. The history match strategy depended only on global modifications using the new commercialized optimization techniques. Previously, model calibration or history matching has commonly been conducted on a single deterministic model by “manual” or “Trial and Error” approach. With recent advances in the application of uncertainty and optimization in numerical reservoir simulation it became a must to assess the effect of different parameters not only during history matching but also its effect on the forecast through producing a probabilistic forecasting (3). The feasibility of using the manual history matching with a single deterministic model has become questionable. A Physically-sound proper set of parameters with realistic ranges are introduced at each stage of the process in logical order and introduced to optimizers to get the best history matched case with a possibility of considering more than one history matched case for a probabilistic forecasting scenarios (4). This paper will present the approach utilized for calibrating the comingled production wells throughout post-production RFT data using the new uncertainty & optimization module to obtain the best global matched model.

This paper discusses the application of an efficient streamline-based inversion method to a large multi-well multi-tracer Partitioning Interwell Tracer Test (PITT) in the McClesky sandstone, 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 in 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.

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.

We explore the use of efficient streamline-based simulation approaches for modeling partitioning interwell tracer tests in hydrocarbon reservoirs. Specifically, we utilize the unique features of streamline models to develop an efficient approach for interpretation and history matching of field tracer response. A critical aspect here is the underdetermined and highly ill-posed nature of the associated inverse problems. We have investigated the relative merits of the traditional history matching ('amplitude inversion') and a novel travel time inversion in terms of robustness of the method and convergence behavior of the solution. We show that the traditional amplitude inversion is orders of magnitude more non-linear and the solution here is likely to get trapped in local minimum, leading to inadequate history match. The proposed travel time inversion is shown to be extremely efficient and robust for practical field applications. The streamline approach is generalized to model water injection in naturally fractured reservoirs through the use of a dual media approach. The fractures and matrix are treated as separate continua that are connected through a transfer function, as in conventional finite difference simulators for modeling fractured systems. A detailed comparison with a commercial finite difference simulator shows very good agreement. Furthermore, an examination of the scaling behavior of the computation time indicates that the streamline approach is likely to result in significant savings for large-scale field applications. We also propose a novel approach to history matching finite-difference models that combines the advantage of the streamline models with the versatility of finite-difference simulation. In our approach, we utilize the streamline-derived sensitivities to facilitate history matching during finite-difference simulation. The use of finite-difference model allows us to account for detailed process physics and compressibility effects. The approach is very fast and avoids much of the subjective judgments and time-consuming trial-and-errors associated with manual history matching. We demonstrate the power and utility of our approach using a synthetic example and two field examples. We have also explored the use of a finite difference reservoir simulator, UTCHEM, for field-scale design and optimization of partitioning interwell tracer tests. The finite-difference model allows us to include detailed physics associated with reactive tracer transport, particularly those related with transverse and cross-streamline mechanisms. We have investigated the potential use of downhole tracer samplers and also the use of natural tracers for the design of partitioning tracer tests. Finally, we discuss several alternative ways of using partitioning interwell tracer tests (PITTs) in oil fields for the calculation of oil saturation, swept pore volume and sweep efficiency, and assess the accuracy of such tests under a variety of reservoir conditions.

The paper describes reservoir management challenges during the first 5 years of the production from the Terra Nova oil field, located offshore Newfoundland and Labrador, Canada. The purpose of the paper is to share lessons learned in the appraisal strategy, data monitoring, subsurface modeling and production management of this subsea/ floating, production, storage, offloading (FPSO) development. The material covers appraisal history, reservoir description, production history, reservoir monitoring, reservoir management, lessons learned and future challenges. The paper will discuss the need for a balanced approach regarding development and delineation of the resources. Since there is no gas export from Terra Nova or any of the Grand Banks fields, surplus gas has to be re-injected. The Terra Nova depletion plan requires that the production from the water injection region and the gas flood regions be balanced for optimum pressure management. The relative richness of the injection gas from the Terra Nova production process has the potential to create miscible gas-oil conditions. We will discuss how slim tube experiments and compositional tracking of the fluids have demonstrated benefits to the oil recovery by operating the gasflood at a reasonable pressure. The Terra Nova field lies not only in a harsh environment but also in an immature, low infrastructure area. This has led to additional challenges regarding reservoir management and future development. Production behavior and history matching have shown that seismically defined fault blocks are transmissible rather than sealing. The presence of leaky faults is beneficial for oil recovery, but presents challenges for precise reservoir simulation modeling and history matching. Terra Nova has extensive amount of data from bottom hole pressure gauges, wireline formation pressures (e.g. RFT/RCI), water cut trends GOR trends, gas tracers, etc. The paper discusses how these data have been integrated in the reservoir management process. Various history matching approaches will be discussed; both manual history matching and assisted techniques (experimental design). This is to the best of our knowledge the first SPE paper from the Grand Banks oil province that describes integrated reservoir management challenges particular to this region.

A particularly efficient flow solver can be obtained by combining a recent mixed multiscale finite-element method for computing pressure and velocity fields 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. 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 the multiscale basis functions are updated infrequently throughout a dynamics 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 geologic 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 subjective judgments and time-consuming trial-and-errors in manual history matching. Moreover, the sensitivities are used to control a procedure for adaptive updating of the basis functions 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 a 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 one million cells with 69 producing wells. Using our proposed approach, history matching over a period of 7 years is accomplished in less than forty minutes on an ordinary workstation PC.

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.

Seismic data incorporation in reservoir simulation models history matching (HM) studies has been continuously growing. 4D seismic data, in contrast with well production data, can provide a very good scenario of fluids arrangement along reservoir. In this work we describe how 3D and 4D seismic data gathered in acquisitions performed in Campos Basin was incorporated in Marlim Sul deep water field geological model reconstruction and in assisted HM (AHM). It is taken advantage of both 3D and 4D seismic data in several stages of the study, for instance, in the construction of a new porosity – most influential in impedance – model by using a methodology based on the inversion of synthetic seismic (calculated by petro-elastic model) in porosity through an optimization process that aims to reduce the difference between observed and synthetic impedance, and when defining influential parameters based on fluids displacement registered by seismic signal, by using a technique based on the creation of transmissibility multipliers parameters regions that considers the fluids displacement shown in 4D signal. Another relevant point is the use of information from reservoir and 3D seismic data when weighting the 4D data in the objective function. Combining the above mentioned techniques with the knowledge of the field – supported by the 3D seismic data – which allowed, for instance, identification of faults – where fault transmissibility multipliers were used as parameters in the HM process – a fairly good agreement on the observed well and seismic production data was achieved. HM studies using AHM tools have been shown a much more time-efficient technique when compared to manual HM. The incorporation of 4D seismic data can considerably improve the HM quality by improving the reservoir description, once it increases the ability of describing fluids arrangement and pressure distribution. The techniques successfully applied in the Marlim Sul field HM support these conclusions.

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.

Monument Butte field produces oil from 22 commingled sandstone reservoirs across a 2200-ft interval of the Green River formation. Total (primary and secondary) recovery is less than 10% of the OOIP. Green River formation sands are lenticular, lacustrine fluvial-deltaic units that have limited aerial extent and vertical communication, resulting in poor interwell connectivity. Moreover, the reservoirs sands have low permeability. To determine interwell connectivity and to identify methods to improve oil recovery, we conducted detailed reservoir characterization and simulation studies in a pilot area of Monument Butte field. We built a 3D geostatistical model and ranked geological realizations using production history matching with streamline simulation. Detailed petrophysical and geological modeling were used to quantify interwell connectivity of sands as a function of well spacing. Studies of interwell connectivity were also used to identify flow barriers that we applied in upscaled reservoir simulation models to better represent interwell connectivity. A combination of manual and assisted history matching methods was used to calibrate the reservoir model. Primary, secondary, and tertiary development strategies were then evaluated for future optimization of oil recovery for new developing areas in Monument Butte field. This study yielded a reservoir model much improved over those of previous studies. A good history match was obtained for most wells, despite the complexity of this stacked, heterogeneous reservoir system. Simulation results indicate that interwell connectivity has the greatest impact on reservoir performance. Interwell connectivity was demonstrated for only major sands, and interwell connectivity increases as well spacing decreases for these sands. A pilot 20-acre infill drilling study is needed to verify this conclusion. The overall connectivity for the combined 22 zones is 0.056 (connected adjacent well pairs divided by total adjacent well pairs). The connectivities for major sand zones evaluated by history matching are 0.65, 0.16, 0.23, and 0.01 for the D1 sand, C sands, A sands, and LODC sands, respectively. Simulation results indicate that reservoir performance can potentially be improved with various reservoir management strategies, including infill drilling. For new development areas, water flooding should start as soon as possible (within 6 months). Tertiary recovery methods may have potential in the Monument Butte field, given the low recovery efficiencies for primary and secondary recovery. Water-alternating-CO2 injection appears to be superior to other tertiary recovery methods. Further evaluation and piloting of tertiary methods is required, given the connectivity risk. The target area study results can be used to guide the development of other parts of the large Monument Butte field.

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.

History matching is the process of calibrating reservoir models with actual production and pressure data. The model is considered calibrated if it can reproduce the historical data of the reservoir it represents. Only history matched models can be used to predict the reservoir future performance under different scenarios. Traditional history matching is carried out through trial and error sequences of adjusting model parameters until a satisfactory match is obtained. History matching is an inverse problem, and hence it may have non-unique solutions. History matching process is an ill posed problem, and it is often very time consuming. In recent years, several approaches have been introduced to assist the history matching process. Assisted history matching concept is inspired by the optimization theory which has been first introduced by Kantorovich in the area of mathematical programming. The history matching problem is converted to an optimization problem with the objective of minimizing the difference between observed and simulated data. The workflow of assisted history matching is usually composed of three main procedures: experimental design, proxy modeling, and optimization. This work introduces a new assisted history matching workflow. The proposed workflow involves introducing a superior technique in each element of the typical assisted history matching workflow. Sobol sequence is the proposed experimental design technique whereas Kriging is used as the proxy modeling method. Three recently developed metaheuristic global optimization algorithms [firefly optimization (FFO), bee colony optimization (BCO), and harmony search optimization (HSO)] are used to optimize the created proxy model. The following part of this work presents the methodology followed to prove the superiority of the newly proposed technique(s) in each step of the three main steps of the assisted history matching procedure. To show the effectiveness of the proposed methods, their performance in solving assisted history matching problems is compared with the latest algorithms commonly used in commercial assisted history matching programs. Three synthetic history matching problems of different reservoir engineering applications with variable complexity are used as test problems to compare the results with each other and validate the work conclusions. A performance indicator is developed to quantify the relative error between the estimated values of history matching parameters using the studied algorithms and the exact solutions. The studied assisted history matching newly proposed techniques are applied on the three test problems and the results of each assisted history matching aspect are presented in the following section.

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

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.

The significant increase in using reservoir simulation models poses significant challenges in the design and calibration of models. Moreover, conventional model calibration, history matching, is usually performed using a trial and error process of adjusting model parameters until a satisfactory match is obtained. In addition, history matching is an inverse problem, and hence it may have non-unique solutions. In typical reservoir simulation problems with a large number of unknown parameters, the problem could be ill-posed. In recent years, assisted history matching approach has been offered to solve this problem hopefully. In this paper, proposes an efficient assisted history matching workflow is recommended, and its application in a full field reservoir simulation model of a mature gas-cap reservoir is presented. In addition, Sobol sequence experimental design technique, Kriging proxy modeling, and three novel metaheuristic optimization algorithms are used to assist the history match of 28 years of historical production and pressure data. Finally, the results are compared with those obtained using a manual history matching procedure and with those obtained using the most widely used assisted history matching workflow. Also, in the proposed workflow, a significant improvement in terms of match quality and solution time is shown.