A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements

We present a Bayesian framework for reconstruction of subsurface hydraulic properties from nonlinear dynamic flow data by imposing sparsity on the distribution of the solution coefficients in a compression transform domain

4D Seismic History Matching Incorporating Unsupervised Learning

The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques

Multiobjective and Level Set Methods for Reservoir Characterization and Optimization

Proper management of oil and gas reservoirs as dynamic systems reduces operational expenditures, alleviates uncertainty, and increases hydrocarbon recovery. In this dissertation, we focus on two issues in reservoir management: multiobjective integration and channelized reservoir calibration. Multiple objectives, including bottom-hole pressure (BHP), water cut, and 4-D seismic data, are utilized in model ranking, history matching, and production optimization. These objectives may conflict, as they represent characteristics coming from different measurements and sources, and, significantly, of varying scales. A traditional weighted-sum method may reduce the solution space, often leading to loss of key information for each objective. Thus, how to integrate multiple objectives effectively becomes critical in reservoir management. This dissertation presents a Pareto-based approach to characterize multiobjective and potentially conflicting features and to capture geologic uncertainty, preserving the original objective space and avoiding weights determination as in the weight-sum method. For channelized reservoirs, identification of the channel geometry and facies boundaries, as well as characterization of channel petrophysical properties are critical for performance predictions. Traditional history matching methods, however, are unable to preserve the channel geometry. We propose a level set based method, integrated with seismic constraint and coupled with the Grid Connectivity Transform (GCT) for channelized reservoirs calibration. We first develop the Pareto-based model ranking (PBMR) to rank multiple realizations, taking into consideration seismic and production data. We demonstrate that this approach can be applied to select multiple competitive realizations compared with the weighted-sum method, and uncertainty range of each objective can be effectively addressed. Next, we extend the Pareto-based framework to full-field history matching and production optimization of the Norne Field in the North Sea. A hierarchical history matching workflow including global and local updates helps to capture the large- and fine-scale heterogeneity. A two-step polymer flood optimization consisting of the streamline-based rate optimization and the Pareto-based polymer optimization is shown to be beneficial for reducing the impact of heterogeneity and increasing production improvement as well as NPV. Finally, we propose a two-step history matching workflow for facies and property calibration of the channelized reservoirs, where the channel geometry is modeled using the level set method, and smaller scale heterogeneity is modeled using the GCT. Moreover, the seismic constraints incorporated into the level set improves facies model calibration

Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media : A critical review

Physical and biogeochemical heterogeneity dramatically impacts fluid flow and reactive solute transport behaviors in geological formations across scales. From micro pores to regional reservoirs, upscaling has been proven to be a valid approach to estimate large-scale parameters by using data measured at small scales. Upscaling has considerable practical importance in oil and gas production, energy storage, carbon geologic sequestration, contamination remediation, and nuclear waste disposal. This review covers, in a comprehensive manner, the upscaling approaches available in the literature and their applications on various processes, such as advection, dispersion, matrix diffusion, sorption, and chemical reactions. We enclose newly developed approaches and distinguish two main categories of upscaling methodologies, deterministic and stochastic. Volume averaging, one of the deterministic methods, has the advantage of upscaling different kinds of parameters and wide applications by requiring only a few assumptions with improved formulations. Stochastic analytical methods have been extensively developed but have limited impacts in practice due to their requirement for global statistical assumptions. With rapid improvements in computing power, numerical solutions have become more popular for upscaling. In order to tackle complex fluid flow and transport problems, the working principles and limitations of these methods are emphasized. Still, a large gap exists between the approach algorithms and real-world applications. To bridge the gap, an integrated upscaling framework is needed to incorporate in the current upscaling algorithms, uncertainty quantification techniques, data sciences, and artificial intelligence to acquire laboratory and field-scale measurements and validate the upscaled models and parameters with multi-scale observations in future geo-energy research.© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)This work was jointly supported by the National Key Research and Development Program of China (No. 2018YFC1800900 ), National Natural Science Foundation of China (No: 41972249 , 41772253 , 51774136 ), the Program for Jilin University (JLU) Science and Technology Innovative Research Team (No. 2019TD-35 ), Graduate Innovation Fund of Jilin University (No: 101832020CX240 ), Natural Science Foundation of Hebei Province of China ( D2017508099 ), and the Program of Education Department of Hebei Province ( QN219320 ). Additional funding was provided by the Engineering Research Center of Geothermal Resources Development Technology and Equipment , Ministry of Education, China.fi=vertaisarvioitu|en=peerReviewed

Data-Driven Modeling and Prediction for Reservoir Characterization and Simulation Using Seismic and Petrophysical Data Analyses

This study explores the application of data-driven modeling and prediction in reservoir characterization and simulation using seismic and petrophysical data analyses. Different aspects of the application of data-driven modeling methods are studied, which include rock facies classification, seismic attribute analyses, petrophysical properties prediction, seismic facies segmentation, and reservoir dimension reduction. The application of using petrophysical well logs to predict rock facies is explored using different data analytics methods including decision tree, random forest, support vector machine and neural network. Different models are trained from a set of well logs and pre-interpreted rock facies data. Among the compared methods, the random forest method has the best performance in classifying rock facies in the dataset. Seismic attribute values from a 3D seismic survey and petrophysical properties from well logs are collected to explore the relationships between seismic data and well logs. In this study, deep learning neural network models are created to establish the relationships. The results show that a deep learning neural network model with multi-hidden layers is capable to predict porosity values using extracted seismic attribute values. The utilization of a set of seismic attributes improves the model performance in predicting porosity values from seismic data. This study also presents a novel deep learning approach to automatically identify salt bodies directly from seismic images. A wavelet convolutional neural network (Wavelet CNN) model, which combines wavelet transformation analyses with a traditional convolutional neural network (CNN), is developed and demonstrated to increase the accuracy in predicting salt boundaries from seismic images. The Wavelet CNN model outperforms the conventional image recognition techniques, providing higher accuracy, to identify salt bodies from seismic images. Besides, this study evaluates the effect of singular value decomposition (SVD) in dimension reduction of permeability fields during reservoir modeling. Reservoir simulation results show that SVD is valid in the parameterization of the permeability field. The reconstructed permeability fields after SVD processing are good approximations of the original permeability values. This study also evaluates the application of SVD on upscaling for reservoir modeling. Different upscaling schemes are applied on the permeability field, and their performance are evaluated using reservoir simulation

Statistical Analyses of Pore Pressure Signals in Claystone During Excavation Works at the Mont Terri Underground Research Laboratory

In many countries (such as Belgium, Germany, France, Japan, Switzerland, and United Kingdom), deep argillaceous formations are considered as potential host rocks for geological disposal of high-level and intermediate-level long-lived radioactive wastes. Some of these countries are investigating the suitability of high compacted clay-rich rocks at depths down to around 500 m below the ground surface. The general disposal concept comprises a network of drifts and tunnels linked to the surface by shafts and ramps, all artificially ventilated. Research is ongoing in Underground Research Laboratories, like the Mont Terri site in the Swiss Jura, to assess and ensure the safety of the repositories for the full decay life of the radioactive waste, i.e. the capacity of the hypothetical repository toprevent the migration of radionuclides towards the biosphere.[...

Oil reservoir characterization using ensemble data assimilation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2008.Pages 211-212 blank.Includes bibliographical references.Increasing world energy demand combined with decreasing discoveries of new and accessible hydrocarbon reserves are necessitating optimal recovery from the world's current hydrocarbon resources. Advances in drilling and monitoring technologies have introduced intelligent oilfields that provide real-time measurements of reservoir conditions. These measurements can be used for more frequent reservoir model calibration and characterization that can lead to improved oil recovery though model-based closed-loop control and management. This thesis proposes an efficient method for probabilistic characterization of reservoir states and properties. The proposed algorithm uses an ensemble data assimilation approach to provide stochastic characterization of reservoir attributes by conditioning individual prior ensemble members on dynamic production observations at wells. The conditioning is based on the second-order Kalman filter analysis and is performed recursively, which is suitable for real-time control applications. The prior sample mean and covariance are derived from nonlinear dynamic propagation of an initial ensemble of reservoir properties. Realistic generation of these initial reservoir properties is shown to be critical for successful performance of the filter. When properly designed and implemented, recursive ensemble filtering is concluded to be a practical and attractive alternative to classical iterative history matching algorithms. A reduced representation of reservoir's states and parameters using discrete cosine transform is presented to improve the estimation problem and geological consistency of the results. The discrete cosine transform allows for efficient, flexible, and robust parameterization of reservoir properties and can be used to eliminate redundancy in reservoir description while preserving important geological features.This improves under-constrained inverse problems such as reservoir history matching in which the number of unknowns significantly exceeds available data. The proposed parameterization approach is general and can be applied with any inversion algorithm. The suitability of the proposed estimation framework for hydrocarbon reservoir characterization is demonstrated through several water flooding examples using synthetic reservoir models.by Behnam Jafarpour.Ph.D

Summary statistics from training images as prior information in probabilistic inversion

A strategy is presented to incorporate prior information from conceptual geological models in probabilistic inversion of geophysical data. The conceptual geological models are represented by multiple-point statistics training images (TIs) featuring the expected lithological units and structural patterns. Information from an ensemble of TI realizations is used in two different ways. First, dominant modes are identified by analysis of the frequency content in the realizations, which drastically reduces the model parameter space in the frequency-amplitude domain. Second, the distributions of global, summary metrics (e.g. model roughness) are used to formulate a prior probability density function. The inverse problem is formulated in a Bayesian framework and the posterior pdf is sampled using Markov chain Monte Carlo simulation. The usefulness and applicability of this method is demonstrated on two case studies in which synthetic crosshole ground-penetrating radar traveltime data are inverted to recover 2-D porosity fields. The use of prior information from TIs significantly enhances the reliability of the posterior models by removing inversion artefacts and improving individual parameter estimates. The proposed methodology reduces the ambiguity inherent in the inversion of high-dimensional parameter spaces, accommodates a wide range of summary statistics and geophysical forward problem