History Matching Under Training-Image-Based Geological Model Constraints

Objectives/Scope: This study focusses on the Wara Formation reservoir in the Greater Burgan Field. The study rapidly assessed the sedimentological and stratigraphic controls on production and water-flood behaviour in the southwest portion of the Wara reservoir in the Burgan Field, which is in the early stages of water-flood development. Methods, Procedures, Process: This eight-week long study delivered a refreshed Wara reservoir zonation scheme in the southwest portion of the Burgan field and new Reservoir Depositional Element Maps. Integration of production data (datum pressures. PLT's-ILT's and tracers), geological data (core, well-logs, analogues) enabled the identification of flow units bounded by significant ‘flooding surface shales’. It also showed that communication between these flow units is highly baffled because of the extensive bounding mudstone layers and complex internal flow unit sand and shale body architecture. Results, Observations, Conclusions: Two main Reservoir Management Units, which are bounded by laterally extensive mudstones that can be correlated across the study area, were identified. The oldest of these is developed in the Lower Wara and comprises multi-storey fluvial/tide-influenced deposits deposited within an incised valley. A prominent flooding surface separates the Lower Wara Formation from the Upper Wara, which comprises tide-influenced proximal delta-front and delta-top deposits. Integration of reservoir geology, pressure and dynamic production data suggests that there are at least two higher-resolution units within the Upper Wara. Recognition of this reservoir architecture has implications for zonal perforation strategies and injector-producer conformance; for example, water injection into the Lower Wara flow unit is highly unlikely to provide pressure support or sweep to either of the Middle Wara flow units. Lateral communication within a given flow unit is expected to be better, but still limited, due to multiple levels of geological heterogeneities. Whilst injectors may provide pressure support over relatively long-distances (-5 km), fluid flow-paths are expected to be complex and tortuous. Novel/Additive Information: The study highlights the value of rapidly integrating diverse subsurface data sets, including core, electric-logs, pressure and production data to assess reservoir layering and sand body architecture, reservoir description uncertainties and their potential impact on production. New subsurface products delivered by the study illustrate how reservoir layering and reservoir architecture strongly impact oil-water contact movement due to production. Coning from a bottom drive aquifer, which occurs in relatively homogenous high net-to-gross reservoirs such as the Burgan Formation, is not a significant issue in the Wara Formation, where there are multiple flow units characterized by internal baffles and barriers. Phenomena such as multiple moved oil water contacts and cusping lead to uneven flood fronts and reduced sweep efficiency.

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.

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.

Time-lapse seismic data has been widely used for the detailed reservoir characterization, and data assimilation algorithms are commonly used for petroleum reservoir history matching of production data. However, hardly any seismic data has been integrated into the reservoir inverse modeling workflow, due to the large data size, finer gridding, and especially the scarcity in time of the seismic data. Popular ensemble-based reservoir inverse modeling methods such as ensemble Kalman filter (EnKF) also face the problems of high computational cost due to the storage of the intermediate variables, restarting the reservoir simulating process and the inconsistency of the full-step and step-wise simulations. The intrinsic sequential data assimilations characteristics of the EnKF set obstacles to assimilate 4D seismic data. The alternative method can be the ensemble smoother (ES), which is an ensemble based method for data assimilation. The ES is based on a Bayesian updating scheme of the reservoir model to match the production history and improve the production forecast. To improve the algorithm convergence, a multiple data assimilation (MDA) method was proposed by Emerick and Reynolds (2012a). The algorithm we used is called ensemble smoother with multiple data assimilation (ESMDA), we modified the ESMDA to integrate the geophysical data, and created the new workflow to history match both the production and geophysical data. The available production data is well measurements, including oil production rate, well water cut and bottom-hole pressure, as well as time-lapse geophysical data, including P-wave impedance. In this paper, we first formulated the mathematical descriptions of ESMDA, we then down-scaled the seismic data, and modified the data assimilation algorithms, illustrated the history matching workflow for both production and geophysical data, finally, we showed how it works with a case study on a water flooding operation in a synthetic reservoir. In comparison, we also showed the history matching only on the production data, which yields an inferior results than the one matches both production and seismic data.

Summary Reservoir management is based on the prediction of reservoir performance by means of numerical-simulation models. Reliable predictions require that the numerical model mimic the production history. Therefore, the numerical model is modified to match the production data. This process is termed history matching (HM). Form a mathematical viewpoint, HM is an optimization problem, where the target is to minimize an objective function quantifying the misfit between observed and simulated production data. One of the main problems in HM is the choice of an effective parameterization—a set of reservoir properties that can be plausibly altered to get a history-matched model. This issue is known as a parameter-identification problem, and its solution usually represents a significant step in HM projects. In this paper, we propose a practical implementation of a multiscale approach aimed at identifying effective parameterizations in real-life HM problems. The approach requires the availability of gradient simulators capable of providing the user with derivatives of the objective function with respect to the parameters at hand. Objective-function derivatives can then be used in a multiscale setting to define a sequence of richer and richer parameterizations. At each step of the sequence, the matching of the production data is improved by means of a gradient-based optimization. The methodology was validated on a synthetic case and was applied to history match the simulation model of a North Sea oil reservoir. The proposed methodology can be considered a practical solution for parameter-identification problems in many real cases until sound methodologies (primarily adaptive multiscale estimation of parameters) become available in commercial software programs.

An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Reservoir model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. In addition, the validation process of the reservoir simulation model often introduces a break of the conceptual connection to the geological model. This raises questions on how to quantify the deviation between the calibrated simulation model and the original geological model. Workflow designs for history matching require scalable and efficient optimization techniques to address project needs. Derivative-free techniques like Markov Chain Monte Carlo (MCMC) are used for optimization and uncertainty quantification. Adjoint techniques derive analytical sensitivities directly from the flow equations. For history matching those sensitivities are efficiently used for property updates on grid block level. Both techniques have different characteristics and support alternative history matching strategies like global vs. local, stochastic vs. deterministic. In this work both techniques will be applied in an integrated workflow design to the Norne field. The Norne field is a North Sea oil-and-gas reservoir with approximately 30 wells, with one third being used for WAG injection for pressure support. Field data was previously released by Statoil and made available for a public benchmark study (NTNU Norway) testing history matching techniques including production and time-lapsed seismic data. We focus on well production data for history matching. MCMC is used for global parameter updates and uncertainty quantification in a Bayesian context. An implementation of an adjoint technique is applied for analytical sensitivity calculations and local parameter adjustments of rock properties. History matching results are presented for field wide and well-by-well production data. Consistency checks between updated and original geological model are presented for rock property distribution maps. Geostatistical measures including spatial correlations are used to quantify deviations between updated and original geological model. In conclusion scalability and performance efficiency of the practical workflow implementation is discussed with a perspective of a consistent feedback loop from history matching to geological modeling.

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

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.

High resolution sequence stratigraphical methods have been used in the detailed geological modeling of an element of the Ness formation of a North Sea field. This updated geological description has been used as input to the stochastic geological model MOHERES. 14 realizations of facies architecture and petrophysical properties have been generated. The stochastically generated realizations have been scaled up to a refined element of an existing deterministic, history matched reservoir simulation model for the full Upper Brent reservoir. The upscaled versions of the realizations have been connected to the full field model, and reservoir simulations have been performed to compare twelve years of production history with simulated results for well production, RFT- and PLT-data. Six of the realizations gave good to very good results when compared to measured production data. These realizations were used for simulation of the future production performance to the year 2010, making it possible to estimate its uncertainty.

Key coalbed methane reservoir properties can be determined using a logical history matching analysis procedure that incorporates laboratory, geologic and production data. Methane and water production data were history matched using a dual-porosity, two-phase finite difference reservoir simulator modified to account for the storage and flow mechanisms unique to coal. Two field examples demonstrate the results and show the applicability of reservoir modeling to assessing the effectiveness of stimulation and predicting well performance.

There exist several methods for history matching of reservoir facies distribution. When using these methods, the facies mean size is usually supposed to be prior information known without uncertainty. However, in reality, it is often difficult to acquire an accurate estimation of the facies mean size due to limited measurement data. Thus, it is more reasonable to treat the facies mean size as an uncertain model parameter for updating. In this work, we propose a methodology to jointly update the mean size and spatial distribution of facies in reservoir history matching. In the parameterization step, we utilize a Gaussian random field and a level set algorithm to parameterize each facies. The range of the Gaussian field controls the facies mean size of the generated facies distribution realizations. To accomplish jointly updating the mean size and spatial distribution of facies, the Gaussian random field is further parameterized by the Karhunen–Loeve (KL) expansion. We choose the range of the Gaussian field and the independent Gaussian random variables in the KL expansion as model parameters. After model parameterization, we use the Levenberg–Marquardt ensemble randomized maximum likelihood filter (LM_EnRML) to perform history matching. Three synthetic cases are set up to test the performance of the proposed method. Numerical results show that for estimating the facies field, the mean size and spatial distribution of facies can be jointly updated to match the reference distribution using the proposed method.

Capillary pressure plays a very important role in naturally fractured reservoirs. The exchange of fluids between matrix and fracture is essentially dependent on capillary pressure expressed by the capillary pressure curve. Capillary force may contribute to the displacement process through imbibition process, or may oppose it in the drainage displacement process. It is essential to represent the capillary pressure curves properly. Many capillary pressure correlations have been suggested in the literature. Capillary pressure data were normally measured using core data. However these methods suffer limitation due to the scale over which the data were collected. Therefore it will be very useful if the capillary pressure curve could be extracted from production data through the history matching process. This paper presents a simple and generalized capillary pressure model which captures both the spontaneous and forced parts of the imbibition capillary pressure curve. In addition, the capillary pressure was estimated by the production data. The sensitivities of production data with respect to capillary pressure model parameters are required when the gradient-based optimization algorithm is used to minimize the objective function. How to calculate the sensitivities of the production data with respect to the capillary pressure model parameters is provided. By minimizing the objective function which describes the mismatch of the observed and simulated production data, the water-oil capillary pressure curves are estimated. All of implementations are incorporated into a commercial simulator (ECLIPSE) and iterated in the automatic history matching scheme. The method is validated using many synthetic cases.

In preparation for the SPE-ATW held in Brugge in June 2008 a unique benchmark project to test the use of flooding optimization and history-matching methods was organized in the form of an interactive competition during the months preceding the ATW. In total nine different groups participated and presented their results during the workshop.Prior to the Brugge workshop, early 2008, a 3D synthetic dataset was made available to the participants by TNO. The dataset consisted of 104 upscaled realizations of a 3D geological model, well-log data from wells with fixed positions, the first 10 years of the production history of the field (including measurement errors), inverted time-lapse seismic data in terms of (uncertain) pressures and saturations, and economic parameters for oil and water (price and discount rate).Participants were asked to provide a history match (either a single matched "best" model or a matched ensemble) based on the available data, and an optimal production strategy (without infill drilling) for the next period (10-20 years). Their strategy was tested on the "real field" to obtain additional production data over the 10-year period. Using these production data, the participants updated their reservoir model and revised their optimal production strategy for the final period of production (20-30 year).The final objective of the exercise was to optimize, within a time constraint, the NPV of a waterflooded oilfield having smart wells that can be controlled by an inflow control valve per completed layer. The results of the nine participants are compared to an optimization of the "real field" as performed by TNO.This paper gives an overview of the results obtained from this benchmark study. After the Brugge workshop the participants that were not able to finalize the exercise in time were given an additional two months time. The results of this additional exercise are reported in this paper as well.

History matching is an important part of reservoir numerical simulation research and the basis for predicting oilfield development performance. At present, integrated smoother and ensemble Kalman filter methods are the mainstream methods of history matching. However, these data assimilation algorithms need to be based on Gaussian assumptions and cannot guarantee the geological authenticity of the modified model. Therefore, to achieve end-to-end processing of dynamic data to modify the reservoir model, while ensuring that the distribution of data does not need to be restricted by Gaussian distribution, this paper proposes the use of the deep conditional generative adversarial network (CGAN) method for automatic reservoir history matching for the first time. The mapping relationship between static geological reservoir parameters and dynamic production data is found through the training generative network, which reduces the uncertainty of the reservoir model obtained by automatic history matching and explains parallel offline processing. The numerical simulation results show that the prediction results on the 2D two-phase flow model are consistent with the results of the proposed deep conditional generative adversarial network method.

Uncertainty quantification is currently one of the leading challenges in the geosciences, in particular in reservoir modeling. A wealth of subsurface data as well as expert knowledge are available to quantify uncertainty and state predictions on reservoir performance or reserves. The geosciences component within this larger modeling framework is partially an interpretive science. Geologists and geophysicists interpret data to postulate on the nature of the depositional environment, for example on the type of fracture system, the nature of faulting, and the type of rock physics model. Often, several alternative scenarios or interpretations are offered, including some associated belief quantified with probabilities. In the context of facies modeling, this could result in various interpretations of facies architecture, associations, geometries, and the way they are distributed in space. A quantitative approach to specify this uncertainty is to provide a set of alternative 3D training images from which several geostatistical models can be generated. In this paper, we consider quantifying uncertainty on facies models in the early development stage of a reservoir when there is still considerable uncertainty on the nature of the spatial distribution of the facies. At this stage, production data are available to further constrain uncertainty. We develop a workflow that consists of two steps: (1) determining which training images are no longer consistent with production data and should be rejected and (2) to history match with a given fixed training image. We illustrate our ideas and methodology on a test case derived from a real field case of predicting flow in a newly planned well in a turbidite reservoir off the African West coast.