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Standard history-matching workflows use qualitative 4D seismic observations to assist in reservoir modeling and simulation. However, such workflows lack a robust framework for quantitatively integrating 4D seismic interpretations. 4D seismic or time-lapse-seismic interpretations provide valuable interwell saturation and pressure information, and quantitatively integrating this interwell data can help to constrain simulation parameters and improve the reliability of production modeling. In this paper, we outline technologies aimed at leveraging the value of 4D for reducing uncertainty in the range of history-matched models and improving the production forecast.
The proposed 4D assisted-history-match (4DAHM) workflows use interpretations of 4D seismic anomalies for improving the reservoir-simulation models. Design of experiments is initially used to generate the probabilistic history-match simulations by varying the range of uncertain parameters (Schmidt and Launsby 1989; Montgomery 2017). Saturation maps are extracted from the production-history-matched (PHM) simulations and then compared with 4D predicted swept anomalies. An automated extraction method was created and is used to reconcile spatial sampling differences between 4D data and simulation output. Interpreted 4D data are compared with simulation output, and the mismatch generated is used as a 4D filter to refine the suite of reservoir-simulation models. The selected models are used to identify reservoir-simulation parameters that are sensitive for generating a good match.
The application of 4DAHM workflows has resulted in reduced uncertainty in volumetric predictions of oil fields, probabilistic saturation S-curves at target locations, and fundamental changes to the dynamic model needed to improve the match to production data. Results from adopting this workflow in two different deepwater reservoirs are discussed. They not only resulted in reduced uncertainty, but also provided information on key performance indicators that are critical in obtaining a robust history match. In the first case study presented, the deepwater oilfield 4DAHM resulted in a reduction of uncertainty by 20% of original oil in place (OOIP) and by 25% in estimated ultimate recoverable (EUR) oil in the P90 to P10 range estimates. In the second case study, 4DAHM workflow exploited discrepancies between 4D seismic and simulation data to identify features necessary to be included in the dynamic model. Connectivity was increased through newly interpreted interchannel erosional contacts, as well as subseismic faults. Moreover, the workflow provided an improved drilling location, which has the higher probability of tapping unswept oil and better EUR. The 4D filters constrained the suite of reservoir-simulation models and helped to identify four of 24 simulation parameters critical for success. The updated PHM models honor both the production data and 4D interpretations, resulting in reduced uncertainty across the S-curve and, in this case, an increased P50 OOIP of 24% for a proposed infill drilling location, plus a significant cycle-time savings.
Simulations of fractured reservoirs are usually performed by dual porosity, dual permeability models. Traditional deterministic workflows that model prominent fracture lineaments often fail to integrate quantification of uncertainty, inherent in fracture spatial distribution and properties. This poses significant challenges on history matching and frequently requires extensive manual beyond geological consistency to achieve the match. We present a method for assisted history matching (AHM) that calibrates models of fractured reservoirs by dynamically updating matrix properties and discrete fracture networks (DFN), while retaining the highest levels of geological consistency and model predictability. The new workflow simultaneously interfaces between applications for building the geo-model and the DFN model "on the fly" and integrates them into a Closed-Loop framework for global stochastic optimization using evolutionary algorithms. Rigorous uncertainty quantification is performed with sensitivity analysis and variability refinement, using the multi-level design of experiments (DoE). We deploy the workflow on the model of a fractured and faulted reservoir developed under natural aquifer drive. Geo-modeling uncertainty workflow generates multiple realizations of seismic-inverted acoustic impedance, used as a 3D trend for populating porosity, with varying variogram parameters. Uncertainty in porosity-permeability correlation coefficients is leveraged to generate multiple, spatially diverse permeability models. Realizations of porosity and permeability are used to generate corresponding realizations of water saturation. By sampling probability distributions of fracture density, geometry, aperture and orientation, 3D realizations of fracture porosity, fracture horizontal and vertical permeability and matrix-fracture transfer parameters are generated. The workflow produces statistically and geologically diverse ensemble of matrix and DFN model realizations that results in excellent variability in dynamic simulation response and confines the observed data. The multi-objective misfit function (OF), subject to minimization in the AHM process, incorporates static well pressures and was evaluated with a reservoir simulator that employs Massive Parallel Processing to achieve practical computation times, even with large-scale simulation grids. The presented AHM workflow demonstrates a unique functionality that enables the integration of DFN geo-mechanical properties (e.g. paleo-stress, pore-pressure) as predictors for fracture network attributes in the process of Closed-Loop model inversion and optimization. The method enables a robust, multivariate reservoir uncertainty quantification and dynamic calibration and delivers geologically consistent fractured reservoir models for reservoir forecasting under uncertainty.
Integration of time-lapse seismic data into dynamic reservoir model is an efficient process in calibrating reservoir parameters update. The choice of the metric which will measure the misfit between observed data and simulated model has a considerable effect on the history matching process, and then on the optimal ensemble model acquired. History matching using 4D seismic and production data simultaneously is still a challenge due to the nature of the two different type of data (time-series and maps or volumes based).
Conventionally, the formulation used for the misfit is least square, which is widely used for production data matching. Distance measurement based objective functions designed for 4D image comparison have been explored in recent years and has been proven to be reliable. This study explores history matching process by introducing a merged objective function, between the production and the 4D seismic data. The proposed approach in this paper is to make comparable this two type of data (well and seismic) in a unique objective function, which will be optimised, avoiding by then the question of weights. An adaptive evolutionary optimisation algorithm has been used for the history matching loop. Local and global reservoir parameters are perturbed in this process, which include porosity, permeability, net-to-gross, and fault transmissibility.
This production and seismic history matching has been applied on a UKCS field, it shows that a acceptalbe production data matching is achieved while honouring saturation information obtained from 4D seismic surveys.
An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross-sectional conductivity map and hence saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this new information into reservoir simulation in combination with other available observations is therefore expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.
The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework under which various sources of available measurements can be readily integrated. Because the assimilation of crosswell EM data can be implemented in different ways (e.g., components of EM fields or inverted conductivity), a comparative study is conducted. The first approach integrates crosswell EM data in its original form which entails establishing a forward model simulating observed EM responses. In this work, the forward model is based on Archie's law that provides a link between fluid properties and formation conductivity, and Maxwell’s equations that describe how EM fields behave given the spatial distribution of conductivity. Alternatively, formation conductivity can be used for history matching, which is obtained from the original EM data through inversion using an adjoint gradient-based optimization method. Because the inverted conductivity is usually of high dimension and very noisy, an image-oriented distance parameterization utilizing fluid front information is applied aiming to assimilate the conductivity field efficiently and robustly. Numerical experiments for different test cases with increasing complexity are carried out to examine the performance of the proposed integration schemes and potential of crosswell EM data for improving the estimation of relevant model parameters. The results demonstrate the efficiency of the developed history-matching workflow and added value of crosswell EM data in enhancing the characterization of reservoir models and reliability of model forecasts.
We present a novel sampling algorithm for characterization and uncertainty quantification of heterogeneous multiple facies reservoirs. The method implements a Bayesian inversion framework to estimate physically plausible porosity distributions. This inversion process incorporates data matching at the well locations and constrains the model space by adding
The proposed workflow uses an ensemble-based Markov Chain Monte Carlo approach combined with sampling probability distributions that are physically meaningful. Moreover, the method targets geostatistical modeling to specific zones in the reservoir. Accordingly, it improves fulfilling the inherent stationarity assumption in geostatistical simulation techniques. Parameter sampling and geostatistical simulations are calculated through an inversion process. In other words, the models fit the known porosity field at the well locations and are structurally consistent within main reservoir compartments, zones, and layers obtained from the seismic impedance volume. The new sampling algorithm ensures that the automated history matching algorithm maintains diversity among ensemble members avoiding underestimation of the uncertainty in the posterior probability distribution.
We evaluate the efficiency of the sampling methodology on a synthetic model of a waterflooding field. The predictive capability of the assimilated ensemble is assessed by using production data and dynamic measurements. Also, the qualities of the results are examined by comparing the geological realism of the assimilated ensemble with the reference probability distribution of the model parameters and computing the predicted dynamic data mismatch. Our numerical examples show that incorporating the seismically constrained models as prior information results in an efficient model update scheme and favorable history matching.
Chen, Hongquan (Texas A&M University) | Yang, Changdong (Texas A&M University) | Datta-Gupta, Akhil (Texas A&M University) | Zhang, Jianye (Institution of Exploration and Development of Tarim Oilfield Company-Petro China) | Chen, Liqun (Institution of Exploration and Development of Tarim Oilfield Company-Petro China) | Liu, Lei (Institution of Exploration and Development of Tarim Oilfield Company-Petro China) | Chen, Baoxin (Institution of Exploration and Development of Tarim Oilfield Company-Petro China) | Cui, Xiaofei (Optimization Petroleum Technology, Inc.) | Shi, Fashun (Optimization Petroleum Technology, Inc.) | Bahar, Asnul (Kelkar and Associates, Inc.)
History matching of million-cell reservoir models still remains an outstanding challenge for the industry. This paper presents a hierarchical multi-scale approach to history matching high resolution dual porosity reservoir models using a combination of evolutionary algorithm and streamline method. The efficacy of the approach is demonstrated through application to a high pressure high temperature (HPHT) fractured gas reservoir in the Tarim basin, China with wells located at an average depth of 7500 meters.
Our proposed multi-scale history matching approach consists of two-stages: global and local. For the global stage, we calibrate coarse-scale static and dynamic parameters using an evolutionary algorithm. The global calibration uses coarse-scale simulations and applies regional multipliers to match RFT data, well bottom hole pressures, and field average pressure. For the local stage, we calibrate fracture permeability using streamline based sensitivities to further match well bottom-hole pressures. The streamlines are derived from the fracture cell fluxes and the sensitivities are analytically computed for highly compressible flow. The sensitivities are validated by comparison with the pertubation method.
The proposed hierarchical multiscale history matching workflow is applied to a faulted and highly fractured deep gas reservoir in the Tarim basin, China. The excessive well cost arising from the large well depth (7500 meters) and high pressure (18000 psi) necessitates optimal field development with limited number of wells. The fracture properties of dual porosity model are upscaled from a highly dense discrete fracture network model generated based on well data and seismic attributes. The history matching includes RFT data, static pressure data and flowing bottom-hole pressure data in producing wells. Field average pressure and RFT (static pressure) data were well matched during the global stage using coarse scale models while flowing bottom-hole pressure is further matched during the local stage calibration using fine scale models. Streamline method has been applied previously mainly to incompressible or slightly compressible flow. However in this application, the results show that the modified streamline-based sensitivity can also significantly reduce data misfit for highly compressible flow. The history matched models are used to visualize well drainage volumes using streamlines. The well drainage volumes in conjunction with static reservoir properties are used to define a ‘depletion capacity map’ which is then used for optimal infill well placement.
The novelty of our approach lies in the application of streamlines derived from dual porosity finite-difference simulation to facilitate history matching and well placement optimization in a tight gas reservoir. The newly developed streamline-based analytical sensitivities are suitable for highly compressible flow. To our knowledge, this is the first time streamlines have been used to facilitate history matching and optimal well placement for gas reservoirs.
History matching is an integral part of field development planning for oil and gas reservoirs. It can be viewed as an inverse modeling technique which utilizes information contained in the observed flow response variables like flow rate, well bottom hole pressure etc. to better quantify the spatial distribution of reservoir model parameters like permeability or porosity. As the reservoir parameters and flow response variables are often related by non-linear relationships, the solution to the inverse problem of history matching is often non-unique. This makes it a problem that can be better handled by stochastic approaches than deterministic approaches. The proposed approach called ‘Indicator-based Data Assimilation’ (InDA) is suitable for such problems. To initiate the process, multiple realizations of the reservoir model parameters are generated based on the available information related to the reservoir description. The ensemble of realizations serves as a measure of initial uncertainty in the spatial distribution of model parameters. Next, flow simulations are run on the ensemble of models and the difference between the observed and simulated flow response variables are used to update the reservoir parameters using InDA. With successive updates, the uncertainty in the model parameters is reduced and the spatial distribution approaches the "true" distribution. Considering the residual uncertainty of the final updated models, reliable field development planning decisions can be made. The proposed method is validated using a realistic reservoir with complex channel-like features emulating a reservoir formed in a fluvial depositional environment. Liquid rate data from existing wells are used for updating reservoir parameters for several time steps using InDA. A comparison of the spatial distribution of final model parameters with the reference model used for validation shows a good match. After the history matching period, existing and new infill wells are run in a forecast mode where the observed and simulated flow responses show a good match. As majority of oil reservoirs comprise of high permeability oil-bearing zones in form of channels passing across low permeability zones, the statistical permeability distribution is bimodal making it non-Gaussian. It is shown that ‘Indicator Transformation’ of variables used in InDA preserves the non-Gaussian structure of the permeability field in comparison to methods like ‘Ensemble Kalman Filter’ (EnKF) that are sub-optimal in such cases.
Nguyen, Ngoc T. B. (University of Calgary) | Dang, Cuong T. Q. (Computer Modeling Group Ltd.) | Yang, Chaodong (Computer Modeling Group Ltd.) | Nghiem, Long X. (Computer Modeling Group Ltd.) | Chen, Zhangxin (University of Calgary)
Steam Assisted Gravity Drainage (SAGD) has been widely applied to unlock hydrocarbon resources in oil sands reservoirs. This method uses steam, which is generated at the surface, to heat a formation and create a steam chamber around an injector. Past studies have indicated that reservoir heterogeneity is one of the crucial factors that directly affectthe performance of the SAGD process. This paper presents an innovative integrated modeling approach for evaluating, assisted history matching, and production forecasting of the SAGD process with the presence of a complex shale barrier system in oil sands reservoirs.
As SAGD is a strongly geological dependent recovery process and, unfortunately, there are many uncertainties associated with reservoir geology in reality. Therefore, it requires generating a large number of geological realizations to capture the critical effects of geology, especially with the presence of shale barriers, in history matching and field development planning of the SAGD process. To quantify the impact and improve the quality of history matching compared with the traditional method, an efficient integrated workflow has been developed in which geological information generated from a geological modeling package is automatically updated for a reservoir simulator and controlled by an intelligent optimizer in a big-loop modeling approach.
A detailed workflow on the integrated modeling approach that includes shale barriers for a typical oil sands reservoir is described in the first section of this paper. Shale bodies are geostatistically distributed in the geological models. A comprehensive parametric study was conducted with numerous geological realizations to identify the critical role of shale barriers in SAGD performance including shale geometry, shale length and thickness, shale distribution and proportions. Then the Bayesian algorithm with a Proxy-based Acceptance-Rejection sampling method is employed for assisted history matching of SAGD production profiles. With the presence of complex shale barriers, it requires simultaneous updating of both geological and reservoir engineering parameters. Using the proposed approach, the global history matching errors were drastically reduced in all production wells. Validation results indicate that the integrated modeling approach effectively helps to update the properties and distribution of shale barriers to find the closest geological distribution compared to the true solution. Finally, an ensemble of the best-matched simulation models is used to perform a probabilistic forecasting to capture the uncertainties in future production profiles.
Not limited to history matching ofthe SAGD process, the proposed approach can be also applied to different complex problems such as robust optimization for various recovery methods from conventional to unconventional reservoirs.
This work focuses on the improvement of an integrated methodology for the automatic history matching of compartmentalised reservoirs using 4D seismic results, stochastic initialization and the Ensemble Kalman Filter method. We show the comparison of two different history matching approaches using the Ensemble Kalman Filter (EnKF) to update the Fault Transmissibility Multipliers (FTM) initially estimated with and without considering the 4D seismic results. In this study, the parameters updated during the history matching are two-phase fault transmissibility multipliers (FTM), absolute permeability and effective porosity of a synthetic realistic 3D reservoir. The true impedance map and the changes in reservoir pressure and saturation were previously computed from 4D seismic results. The systematic estimation of two-phase fault transmissibility multipliers is based on the integration of the collected 4D seismic results and an established method validated in our previous work based on a deterministic model, using the gradient-based History Matching, Levenberg Marquardt method (LM). We present the history matching of a synthetic reservoir using the Ensemble Kalman Filter (EnKF) considering the 4D seismic results to update the models and geostatistical techniques to produce the initial geological models. The stochastic method used is the Sequential Gaussian Simulation (SGS) technique to generate 100 initial models. During history matching using the EnKF, the saturation distributions are computed from the forward modelling of a two-phase system (oil-water). The impedance maps are then estimated using the Gassmann equation and compared with the true impedance map as part of the History Matching process. To validate the results, the cost function consisting of two components is calculated, the first is the structural similarity index of the two reconstructed impedance images to the real impedance image and the second is the RMS cost function value,