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Collaborating Authors
Results
Building Trust in History Matching: The Role of Multidimensional Projection
Hajizadeh, Yasin (Department of Computer Science, University of Calgary) | Amorim, Elisa Portes (Department of Computer Science, University of Calgary) | Costa Sousa, Mario (Department of Computer Science, University of Calgary)
Abstract Assisted history matching frameworks powered by stochastic population-based sampling algorithms have been a popular choice for real-life reservoir management problems for the past decade. These methods provide an ensemble of history-matched models which can be used to quantify the uncertainty of future field performance. As a critique, population-based algorithms are generally considered black-boxes with little knowledge of their performance during history matching. In most cases, the misfit value is used as the only criteria to monitor the sampling algorithms and assess their quality. This paper applies three recently developed multidimensional projection schemes as a novel interactive, exploratory visualization tool for gaining insights to the sampling performance of population-based algorithms and comparing multiple runs in history matching. We use Least Square Projection (LSP), Projection by Clustering (ProjClus) and Principle Component Analysis (PCA) to examine the relationship between exploration of search space and the uncertainty in predictions of reservoir production. These projection techniques provide a mapping of the high dimensional search space into a 2D space by trying to maintain the distance relationships between sampled points. The application of multidimensional projection is illustrated for history matching of the benchmark PUNQ-S3 model using ant colony, differential evolution, particle swarm and the neighbourhood algorithms. We conclude that multi-dimensional projection algorithms are valuable diagnostic tools that should accompany assisted history matching workflows in order to evaluate their performance and compare ensembles of history-matched models. Using the projection tools, we show that misfit value - as an indicator of match quality - is not the only important factor in making reliable predictions. We demonstrate that exploration of the search space is also a critical element in the uncertainty quantification workflow which can be monitored with multidimensional projection schemes.
Abstract Time-lapse (4D) seismic data can be integrated into history matching by comparing predicted and observed data in various domains. These include the time domain (time traces), seismic attributes, or petro-elastic properties such as acoustic impedance. Each domain requires different modelling methods and assumptions as well as data handling workflows. The aim of this work is to investigate the degree to which the choice of domain influences theoutcome of history matching on the choice of best model and associated uncertainties. Another aspect of history matching is that long simulations often pose an obstacle for an automatic approach. In this study we use appropriately upscaled models manageable in the automatic history matching loop. We apply manual and assisted seismic history matching to the Schiehallion field. In the assisted approach, the optimization loop is driven by a stochastic algorithm, while the manual workflow is based on a qualitative comparison of 4D seismic maps. By upscaling we obtained an order of magnitude gain in performance. Accurate upscaling was ensured by thorough volume and transmissibility calculation within regions. The parameterisation of the problem is based on a pattern of seismically derived geobodies with specified transmissibility multipliers between the regions. Seismic predictions are made through petro-elastic modelling, 1D convolution, coloured inversion and calculation of different attributes. We were able to achieve a reasonable match of production and 4D seismic data using coarse scale models in manual and assisted approaches. We observed that the misfit surfaces are different when working in the various seismic domains considered. Use of equivalent domains for observed and predicted data was found to give a more unique misfit response and better result. Accurate comparison of predicted and observed 4D seismic data in different domains is necessary for tackling non-uniqueness of the inverse problem and hence reducing the uncertainty of field development predictions.
- Europe > United Kingdom > Atlantic Margin > West of Shetland (0.35)
- North America > United States > Texas > Harris County > Houston (0.28)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying (1.00)
- North America > United States > Louisiana > East Gulf Coast Tertiary Basin > Bay Marchand Field (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 22/7 > Nelson Field > Forties Formation (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 22/6a > Nelson Field > Forties Formation (0.99)
- (17 more...)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
Adjoint-based History-Matching of Production and Time-lapse Seismic Data
Van Essen, G. M. (Shell Global Solutions International (SGSI)) | Jimenez, E. A. (Shell International E) | Przybysz-jarnut, J. P. (SGSI) | Horesh, L.. (IBM) | Douma, S. G. (Shell Technology Oman) | van den Hoek, P. J. (SGSI, A. Conn) | Conn, A.. (IBM) | Mello, U. T. (IBM)
Abstract Time-lapse (4D) seismic attributes can provide valuable information on the fluid flow within subsurface reservoirs. This spatially-rich source of information complements the poor areal information obtainable from production well data. While fusion of information from the two sources holds great promise, in practice, this task is far from trivial. Joint Inversion is complex for many reasons, including different time and spatial scales, the fact that the coupling mechanisms between the various parameters are often not well established, the localized nature of the required model updates, and the necessity to integrate multiple data. These concerns limit the applicability of many data-assimilation techniques. Adjoint-based methods are free of these drawbacks but their implementation generally requires extensive programming effort. In this study we present a workflow that exploits the adjoint functionality that modern simulators offer for production data to consistently assimilate inverted 4D seismic attributes without the need for re-programming of the adjoint code. Here we discuss a novel workflow which we applied to assimilate production data and 4D seismic data from a synthetic reservoir model, which acts as the real - yet unknown - reservoir. Synthetic production data and 4D seismic data were created from this model to study the performance of the adjoint-based method. The seamless structure of the workflow allowed rapid setup of the data assimilation process, while execution of the process was reduced significantly. The resulting reservoir model updates displayed a considerable improvement in matching the saturation distribution in the field. This work was carried out as part of a joint Shell-IBM research project.
- North America > United States (0.28)
- Europe (0.28)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
Abstract This presentation outlines an integrated workflow that incorporates 4D seismic data into the Ekofisk field reservoir model history matching process. Successful application and associated benefits of the workflow benefits are also presented. A seismic monitoring programme has been established at Ekofisk with 4D seismic surveys that were acquired over the field in 1989, 1999, 2003, 2006 and 2008. Ekofisk 4D seismic data is becoming a quantitative tool for describing the spatial distribution of reservoir properties and compaction. The seismic monitoring data is used to optimize the Ekofisk waterflood by providing water movement insights and subsequently improving infill well placement. Reservoir depletion and water injection in Ekofisk lead to reservoir rock compaction and fluid substitution. These changes are revealed in space and time through 4D seismic differences. Inconsistencies between predicted 4D differences (calculated from reservoir model output) and actual 4D differences are therefore used to identify reservoir model shortcomings. This process is captured using the following workflow: (1) prepare and upscale a geologic model, (2) simulate fluid flow and associated rockphysics using a reservoir model, (3) generate a synthetic 4D seismic response from fluid and rock physics forecasts, and (4) update the reservoir model to better match actual production/injection data and/or the 4D seismic response. The above-mentioned Seismic History Matching (SHM) workflow employs rock-physics modeling to quantitatively constrain the reservoir model and develop a simulated 4D seismic response. Parameterization techniques are then used to constrain and update the reservoir model. This workflow updates geological parameters in an optimization loop through minimization of a misfit function. It is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool using evolutionary algorithm. In summary, the Ekofisk 4D SHM workflow is a multi-disciplinary process that requires collaboration between geological, geomechanical, geophysical and reservoir engineering disciplines to optimize well placement and reservoir management.
- North America > United States > Texas (0.68)
- Europe > Norway > North Sea > Central North Sea (0.27)
- Geology > Structural Geology (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying (1.00)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6507/8 > Heidrun Field > Åre Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6507/8 > Heidrun Field > Tilje Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6507/8 > Heidrun Field > Ile Formation (0.99)
- (22 more...)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.88)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
Abstract Produced Water Chemistry (PWC) has been included in the history matching of reservoir simulations. Generally, in conventional history matching, the water chemistry is not considered as an extra constraint. The chemistry of the different types of water in a reservoir, such as aquifer, connate and seawater is very different, and can be traceable. Produced Water Chemistry is the main source of information to monitor scale precipitation in oil field operations. The objective of this paper is to evaluate the effect of adding produced water chemistry information as an extra constraint history matching a modified version of the PUNQ-S3 reservoir model. The PUNQ-S3 model is a synthetic benchmark case that has been used previously for history matching and uncertainty quantification. Conventional historical production data (gas, oil rates and pressure) from six production wells are supported by the water chemistry tracer data from the wells that produce water in the history period. The different types of water are traced through their distinctive chemistries, namely aquifer, connate (formation) and sea (injection) water. Geological model is matched by varying porosity and permeability, both horizontal (kh) and vertical (kv) according to the prior beliefs about the reservoir geology (layering, spatial correlation and anisotropy). Two history matching (HM) scenarios are considered: including and not-including the Produced Water Chemistry (PWC) as an extra matching constraint. Stochastic Particle Swarm Optimization (PSO) algorithm is used to generate ensembles of history matched models, which characterise the uncertainty of the reservoir prediction. Comparison of the two scenarios reveals potential value of adding PWC data in history matching that allows achieving better matched models and ensuring diversity of HM models, which is essential for robust uncertainty quantification of the predictions.
- Europe (0.93)
- Asia (0.68)
- North America > United States > Texas (0.47)
On Population Diversity Measures of the Evolutionary Algorithms used in History Matching
Abdollahzadeh, Asaad (Heriot-Watt University) | Reynolds, Alan (Heriot-Watt University) | Christie, Mike (Heriot-Watt University) | Corne, David (Heriot-Watt University) | Williams, Glyn (BP) | Davies, Brian (BP)
Abstract In history matching, the aim is to generate multiple good-enough history-matched models with a limited number of simulations which will be used to efficiently predict reservoir performance. History matching is the process of the conditioning reservoir model to the observation data; is mathematically ill-posed, inverse problem and has no unique solution and several good solutions may occur. Numerous evolutionary algorithms are applied to history matching which operate differently in terms of population diversity in the search space throughout the evolution. Even different flavours of an algorithm behave differently and different values of an algorithm's control parameters result in different levels of diversity. These behaviours vary from explorative to exploitative. The need to measure population diversity arises from two bases. On the one hand maintaining population diversity in evolutionary algorithms is essential to detect and sample good history-matched ensemble models in parameter search space. On the other hand, since the objective function evaluations in history matching are computationally expensive, algorithms with fewer total number of reservoir simulations in result of a better convergence are much more favourable. Maintaining population's diversity is crucial for sampling algorithm to avoid premature convergence toward local optima and achieve a better match quality. In this paper, we introduce and use two measures of the population diversity in both genotypic and phenotypic space to monitor and compare performance of the algorithms. These measures include an entropy-based diversity from the genotypic measures and a moment of inertia based diversity from the phenotypic measures. The approach has been illustrated on a synthetic reservoir simulation model, PUNQ-S3, as well as on a real North Sea model with multiple wells. We demonstrate that introduced population diversity measures provide efficient criteria for tuning the control parameters of the population-based evolutionary algorithms as well as performance comparison of the different algorithms used in history matching.
- North America > United States (1.00)
- Europe > United Kingdom > North Sea (0.25)
- Europe > Norway > North Sea (0.25)
- (2 more...)
Abstract 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.
- Phanerozoic > Paleozoic > Devonian (1.00)
- Phanerozoic > Paleozoic > Carboniferous > Mississippian > Middle Mississippian > Visean (0.68)
- Phanerozoic > Paleozoic > Permian > Cisuralian > Asselian (0.48)
- Geology > Sedimentary Geology > Depositional Environment (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Structural Geology > Tectonics (0.93)
- (2 more...)
- Asia > Kazakhstan > West Kazakhstan > Uralsk Region > Precaspian Basin > Karachaganak Field (0.99)
- Asia > Kazakhstan > West Kazakhstan > Precaspian Basin (0.99)
- Asia > Kazakhstan > Mangystau Oblast > Precaspian Basin > Tengiz Field > Tengiz Formation (0.99)
- (32 more...)
Abstract Over the last decade the ensemble Kalman filter (EnKF) has attracted attention as a promising method for solving the reservoir history matching problem: Updating model parameters so that the model output matches the measured production data. The method possesses unique qualities such as; it provides real time update and uncertainty quantification of the estimate, it can estimate any physical property at hand, and it is easy to implement. The method does, however, have its limitations; in particular it is derived based on an assumption of a Gaussian distribution of variables and measurement errors. A recent method proposed to improve upon the original EnKF method is the Adaptive Gaussian mixture filter (AGM). The AGM loosens up the requirements of a linear and Gaussian model by making a smaller linear update than the EnKF and by including importance weights associated with each ensemble member at computational costs as low as EnKF. In this paper we present a refined AGM algorithm where the importance weights are included in the calculation of the apriori and the aposteriori covariance matrices. Moreover, in this paper the AGM algorithm is for the first time applied to a real field study. To validate the performance of AGM the results are compared with the EnKF, with and without distance based localization. Several statistical measures are used to validate the performance of the filters, and we are able to distinguish the performance of the different filters. In particular all the methods provide good history match, but we see that the AGM stands out by better honoring the original geostatistics and by providing consistent predictions when rerun is performed.
- North America > United States (0.46)
- Europe (0.46)
- Africa (0.28)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Abstract An integrated optimization workflow was developed to characterize seismic and sub-seismic fault networks from history-matching. A fractal model of fault networks is optimized via the gradual deformation of stochastic realizations of fault density maps, fault spatial and length distributions. In order to facilitate the history-matching, connectivity analysis tools were developed for characterizing wells-reservoir and well-to-well connectivity. Indeed these connectivity properties usually depend on the fault network realization and may be strongly correlated with the reservoir flow dynamics. Connectivity analyses were performed on a fractured reservoir model involving a five-spot well configuration with four injectors and one producer. The connectivity was estimated from shortest path algorithms applied on a graph representation of the reservoir model. Several reservoir simulations were performed for different fault network realizations to seek correlations between injector-producer connectivity and water breakthrough time. The impact of the fracture properties uncertainties on the wells-reservoir connectivity was estimated via the cumulated connected volume computed for each well. This connectivity measure provides a mean to characterize and classify fault network realizations. Correlations were found between the water breakthrough time and the injector-producer connectivity, thus allowing one to identify the most probable fault network realizations to match the observed water breakthrough time. Finally, for a given fault network realization, it is shown how the oil recovery can be optimized by correlating injectors rates with the injector-producer connectivity. A gain of 3.106 m3 in produced oil was obtained, while retarding the water breakthrough time by 16 years, compared with a case where all injectors have the same rate. The proposed methodology and tools facilitate the history-matching of fractured reservoir, providing consistent reservoir models that can be used for production forecast and optimization.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
Abstract This paper shows the application of two ensemble-based assimilation methods, the Ensemble Kalman filter (EnKF) and the Ensemble Smoother (ES), to constrain an underground gas storage site to well pressure data. The EnKF is a sequential data assimilation method that provides an ensemble of models constrained to dynamic data. It entails a two-step process applied any time data are collected. First, production responses are computed for every model within the ensemble until the following acquisition time. Second, models are updated using the Kalman filter to reproduce the data measured at that time. The EnKF has been widely applied in petroleum industry. More recently, the ES was successfully applied to a real field case. This method is also based on the Kalman filter, but the update is performed globally over the entire history-matching period: values simulated at assimilation times are considered all together in the update step. The uncertain parameters considered here are the porosity and horizontal permeability values populating several layers of the geological model. Applying both the ES and EnKF methods, the spread within the ensembles is reduced and the predictions based on the ensembles of updated petrophysical distributions get closer to the pressure data corresponding to the history-matching and prediction periods.