Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Results
Abstract Current theoretical formulations of assisted history matching (AHM) problems within the Bayesian framework, e.g., ensemble Kalman filter (EnKF) and randomized maximum likelihood (RML), are typically based on the assumption that simulation models can accurately reproduce field data within the measurement error. However, this assumption does not hold for AHM problems of real assets. This paper critically investigates the impact of using realistic, inaccurate simulation models. In particular it demonstrates the risk of underestimating uncertainty, when conditioning real-life models to large numbers of field data. Even though it is well-known, that model error and under-modeling impacts Bayesian methods, the practical effect that uncertainty may be severely underestimated, simply by using all available data is not well appreciated. Besides highlighting this effect, also a mitigation strategy to counteract this problem will be proposed and shown to be effective for the analytical toy model as well as for the real field case used as tests in this paper. After briefly reviewing the Bayesian method and its underlying assumptions, limitations of AHM approaches within the Bayesian framework are analyzed using a simple analytical model in which forecast uncertainty can be computed both with and without constraints due to historic data. In particular the model can be used to illustrate the impact of using an inaccurate, or incomplete, simulation model. The observations from this analytical work can then be generalized to real-life workflows that are currently implemented in many commercial and proprietary tools. To mitigate the observed problem, a fairly simple but effective modification of the AHM workflow is proposed and tested on the analytical test case. The same mitigation procedure is then also applied to improve uncertainty quantification of production forecasts using a real asset model. In order to see if the proposed workflow indeed leads to a more credible uncertainty assessment for forecast results, a specific realization of the asset model is used to generate synthetic production data. The model used for history matching and uncertainty quantification uses a different geological realization and hence can never reproduce the production results (which are assumed to have negligible noise). Also in this realistic setting, it is shown that forecasts easily can be under-estimated when large numbers of data are used to constrain forecast uncertainty in an imperfect model with accurate data. This undesired effect comes out as the flip-side of the attractive property of Bayesian methods that model parameters can be inferred with increased accuracy if the number of data is increased in a perfect model with noisy data.
- Asia > Middle East (0.67)
- North America > United States > Texas (0.46)
Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs
Honorio, Jean (MIT) | Chen, Chaohui (Shell International Exploration and Production, Inc.) | Gao, Guohua (Shell Global Solutions (US) Inc.) | Du, Kuifu (Shell Brasil Exploration and Production) | Jaakkola, Tommi (MIT)
Abstract It is a common practice to reduce the number of parameters that are used to fully describe a static geological model for assisted-history-matching (AHM) of geologically complex reservoirs. However, a model reconstructed from the reduced parameters may often be distorted from prior geological information, especially when discrete facies indicator presents in the model; for example, a reconstructed โchannelโ does not look like a channel. This paper presents a novel machine learning (ML) method that learns prior geological information/data, and then reconstructs a model after pluri-principal-component-analysis (pluri-PCA) is applied. The main steps of the methods are: first, a dictionary of object-based channelized geological models is generated based on the prior geological data/information. A pluri-PCA approach is applied to reduce the dimensions of grid-based static model and to convert the facies models to Gaussian PCA-coefficients. Second, the PCA coefficients are tuned during history matching process and the pluri-Gaussian rock-type-rule is applied to reconstruct the complex geological facies model from the tuned coefficients. Finally, a ML technique called โPiecewise Reconstruction from a Dictionaryโ (PRaD), which is based on the Markov Random Field method, is introduced to minimize the feature distance between the reconstructed model and the training models. In order to enforce geological plausibility, the facies models are reconstructed or regenerated by putting together pieces from different patches in the training realizations. An AHM workflow with the above described new method has been applied to a real turbidite channelized reservoir. The prior geological model indicates that there is clear sand deposition between a gas injector and oil producers. However, one of the production wells has been observed much less gas production than simulated result. Without adding the plausiable additonal fault, the AHM results convinced that the reasonable match on gas production can only be achieved by changing channel orientation and shales/facies distribution. In addition, the new method is observed to preserve both channel features and geostatistics of the model parameters (e.g. facies, permeability, porosity). The additional uncertainties in dynamic aspects (e.g. aquifer strength, relative permeability multipliers, etc.) will be included in AHM workflow and addressed by a derivative-free optimization approach. The new method is able to leverage the prior information provided by geologists in order to produce a non-Gaussian geologically plausible facies model that matches the observation data. While the pluri-PCA reconstruction process helps to preserve the major features and facies fraction within the geological model description, the PRaD method recaptures the missing details of minor features and enables the final model to closely link to the training realizations. Unlike the conventional approach, e.g. adding artificial flow barrier, this method renders the whole history matching workflow applicable to practical problems. In summary, the proposed method can further enhance the quality of the model reconstructed from a training dictionary of geological models.
Enhanced Reparameterization and Data-Integration Algorithms for Robust and Efficient History Matching of Geologically Complex Reservoirs
Gao, Guohua (Shell Global Solutions (US) Inc.) | Vink, Jeroen C. (Shell Global Solutions (US) Inc.) | Chen, Chaohui (Shell International Exploration & Production Inc.) | Alpak, Faruk O. (Shell International Exploration & Production Inc.) | Du, Kuifu (Shell Brasil Exploration and Production)
Abstract It is extremely challenging to design effective assisted history matching (AHM) methods for complex geological models with discrete facies types. History matching then requires minimizing a data mismatch objective, while gradually changing facies types in a way that preserves geological realism. The recently introduced pluri-principal-component-analysis (pluri-PCA) technique effectively overcomes this challenge and furthermore reduces the number of AHM model parameters from millions to hundreds. This drastic reduction of the number of parameters for AHM makes it possible to apply efficient data-integration methods that employ perturbation-based derivatives or derivative-free techniques. In this paper, a novel AHM approach is developed that combines pluri-PCA with a highly efficient data mismatch minimization technique. A parallelized direct-pattern-search (DPS) approach with auto-adaptive pattern size updating is developed to guarantee the convergence of the data mismatch minimization, when the objective function becomes non-smooth due to numerical noise. A trust region variant of the Gauss-Newton (GN) or quasi-Newton (QN) method is effectively hybridized with the DPS method to further enhance the performance by taking into account the smoothness features of the de-noised objective function or data when applicable. The new approach is applied to a synthetic case where the truth is known and a real channelized turbidite reservoir model with three facies types. The model parameters subject to AHM include the PCA-coefficients, which automatically reconstruct the facies indicators and permeability, porosity, and net-to-gross maps. Additional matching parameters include the aquifer strength, fault transmissibility, etc. For the synthetic case, the observed data are obtained by adding Gaussian random noise with zero mean and known variance to the data generated with a truth case simulation. Numerical tests indicate that the hybrid GN-DPS algorithm performs the best among all tested algorithms. The history-matched reservoir models obtained with the new AHM approach (GN-DPS) honor the production measurements with good accuracy. The latter is validated in the synthetic test case by the fact that the normalized objective function converges to a value close to the theoretically expected value of one. For both the synthetic and real cases, the history-matched reservoir models preserve geological realism. Compared to quasi-Newton approaches and the traditional Gauss-Newton (GN) approach using gradient approximated by finite difference with small constant perturbation size, the proposed new AHM approach is more efficient both in terms of the total number of iterations and objective function evaluations (through simulation) required to satisfy a preset convergence criterion. Furthermore, as indicated by the data-mismatch convergence profiles generated from the final history-matched models, the GN-DPS method converges to models with much lower data-mismatch values when compared with traditional methods.
- North America > United States > Texas (0.68)
- Europe (0.67)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Search (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.66)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.46)
An Efficient Optimization Work Flow for Field-Scale In-Situ Upgrading Developments
Gao, Guohua (Shell Global Solutions US Incorporated) | Vink, Jeroen C. (Shell Global Solutions US Incorporated) | Alpak, Faruk O. (Shell International Exploration and Production Incorporated) | Mo, W.. (Shell (China) P&T Limited)
Summary In-situ upgrading process (IUP) is an attractive technology for developing unconventional extraheavy-oil reserves. Decisions are generally made on field-scale economics evaluated with dedicated commercial tools. However, it is difficult to conduct an automated IUP optimization process because of unavailable interface between the economic evaluator and commercial simulator/optimizer, and because IUP is such a highly complex process that full-field simulations are generally not feasible. In this paper, we developed an efficient optimization work flow by addressing three technical challenges for field-scale IUP developments. The first challenge was deriving an upscaling factor modeled after analytical superposition formulation; proposing an effective method of scaling up simulation results and economic terms generated from a single-pattern IUP reservoir-simulation model to field scale; and validating this approach numerically. The second challenge was proposing a response-surface model (RSM) of field economics to analytically compute key field economical indicators, such as net present value (NPV), by use of only a few single-pattern economic terms together with the upscaling factor, and validating this approach with a commercial tool. The proposed RSM approach is more efficient, accurate, and convenient because it requires only 15โ20 simulation cases as training data, compared with thousands of simulation runs required by conventional methods. The third challenge is developing a new optimization method with many attractive features: well-parallelized, highly efficient and robust, and with a much-wider spectrum of applications than gradient-based or derivative-free methods, applicable to problems without any derivative, with derivatives available for some variables, or with derivatives available for all variables. This work flow allows us to perform automated field IUP optimizations by maximizing a full-field economics target while honoring all field-level facility constraints effectively. We have applied the work flow to optimize the IUP development of a carbonate heavy-oil asset. Our results show that the approach is robust and efficient, and leads to development options with a significantly improved field-scale NPV. This work flow can also be applied to other kinds of pattern-based field developments of shale gas and oil, and thermal processes such as steamdrive or steam-assisted gravity drainage.
- Europe (0.93)
- Asia (0.93)
- North America > United States > Texas (0.67)
- Africa > Middle East > Libya > Wadi al Hayat District > Murzuq Basin > Block NC 186 > I&R Fields > R Field > Mamouniyat Formation (0.99)
- Africa > Middle East > Libya > Wadi al Hayat District > Murzuq Basin > Block NC 115 > I&R Fields > R Field > Mamouniyat Formation (0.99)
- North America > United States > Texas > East Texas Salt Basin > Shell Field (0.97)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Thompson Field (0.93)
Integrated Field-scale Production and Economic Evaluation under Subsurface Uncertainty for the Pattern-driven Development of Unconventional Resources
Gao, Guohua (Shell Global Solutions (US) Inc.) | Vink, Jeroen C. (Shell Global Solutions (US) Inc.) | Alpak, Faruk O. (Shell International Exploration & Production Inc.)
Abstract The In-situ Upgrading Process (IUP) is a thermal recovery technique that relies on pattern-based development process, a complicated physical process that involves thermal and mass transfer in porous media, which renders full-field-scale reservoir simulations impractical. Although it is feasible to quantify the impact of subsurface uncertainties on recovery for small-scale sector models via experimental design (ED), it is still a very challenging problem to quantify their impact on field-scale quantities. Straightforward upscaling to field scale does not work, because such conventional superposition-based methods do not capture the effects of spatial variability in rock and fluid properties and the time delay in sequential pattern development. In this paper, we show that, under certain mild assumptions, an analytical superposition formulation can be developed that propagates the uncertainties of production forecasts and economic evaluations generated from a sector model to full field-scale quantities. This formulation can be further simplified such that the variance of a field-scale quantity is analytically expressed as the variance of the same single-pattern quantity multiplied by a (computable) scale-up factor. This makes it possible to implement a practical uncertainty quantification workflow, in which single-pattern results are upscaled to accurate full field results with reliable uncertainty ranges, without the need for full field-scale simulations. We apply the proposed novel superposition and uncertainty propagation method to a multi-pattern IUP development, and demonstrate that this workflow produces reliable results for field-scale production and economics as well as realistic uncertainty ranges. Moreover, these results indicate that the scale-up factor for single-pattern results can accurately capture the impact of spatial correlations of subsurface uncertainties, the size of the field-scale model, the time-delay in pattern development and the discount rate. Uncertainty quantification of field-scale production and economics is a key factor for the successful development of unconventional resources such as extra-heavy oil and oil shale with significant rewards in terms of risk management and project profitability. With minor modifications, the proposed method can also be applied to other pattern-driven processes such as the In-situ Conversion Process (ICP) and Steam Assisted Gravity Drainage (SAGD).
- Geology > Rock Type > Sedimentary Rock (0.54)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (0.35)
- Africa > Middle East > Libya > Wadi al Hayat District > Murzuq Basin > Block NC 186 > I&R Fields > R Field > Mamouniyat Formation (0.99)
- Africa > Middle East > Libya > Wadi al Hayat District > Murzuq Basin > Block NC 115 > I&R Fields > R Field > Mamouniyat Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Thompson Field (0.93)