Calculating derivatives for automatic history matching

Automatic history matching is based on minimizing an objective function that quantifies the mismatch between observed and simulated data. When using gradient-based methods for solving this optimization problem, a key point for the overall procedure is how the simulator delivers the necessary derivative information. In this paper, forward and adjoint methods for derivative calculation are discussed. Procedures for sensitivity matrix building, sensitivity matrix and transpose sensitivity matrix vector products are fully described. To show the usefulness of the derivative calculation algorithms, a new variant of the gradzone analysis, which tries to address the problem of selecting the most relevant parameters for a history matching, is proposed using the singular value decomposition of the sensitivity matrix. Application to a simple synthetic case shows that this procedure can reveal important information about the nature of the history-matching problem.     

重磁异常数据三维人机联作模拟

在研究三角形多面体模型重、磁异常三维正演和反演技术的基础上,吸取人机交互正演模拟的优点,实现了三角形多面体模型重、磁异常数据三维人机联作反演。通过研究三角形多面体模型节点偏导数的计算方法,对目标函数进行线性化处理,形成了计算机自动迭代修改模型体的技术。利用计算机图形技术,在三维空间显示重、磁场和模型体,开发了模型的交互修改技术,使数据解释过程中,可以结合已知信息及人的推断和经验,完成重、磁异常数据的三维模拟,减少了数据解释结果的不确定性。     

Swarm intelligence-based solver for parameter estimation of laboratory through-diffusion transport of contaminants

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated by fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision.     

Approximate Derivative Computations for the Gradient-Based Optimization Methods in the Local Gradual Deformation for History Matching

Reservoir characterization needs the integration of various data through history matching, especially dynamic information such as production or four-dimensional seismic data. To update geostatistical realizations, the local gradual deformation method can be used. However, history matching is a complex inverse problem, and the computational effort in terms of the number of reservoir simulations required in the optimization procedure increases with the number of matching parameters. History matching large fields with a large number of parameters has been an ongoing challenge in reservoir simulation. This paper presents a new technique to improve history matching with the local gradual deformation method using the gradient-based optimizations. The new approach is based on the approximate derivative calculations using the partial separability of the objective function. The objective function is first split into local components, and only the most influential parameters in each component are used for the derivative computation. A perturbation design is then proposed to simultaneously compute all the derivatives with only a few simulations. This new technique makes history matching using the local gradual deformation method with large numbers of parameters tractable.     

An agent-based model for risk-based flood incident management

Effective flood incident management (FIM) requires successful operation of complex, interacting human and technological systems. A dynamic agent-based model of FIM processes has been developed to provide new insights which can be used for policy analysis and other practical applications. The model integrates remotely sensed information on topography, buildings and road networks with empirical survey data to fit characteristics of specific communities. The multiagent simulation has been coupled with a hydrodynamic model to estimate the vulnerability of individuals to flooding under different storm surge conditions, defence breach scenarios, flood warning times and evacuation strategies. A case study in the coastal town of Towyn in the United Kingdom has demonstrated the capacity of the model to analyse the risks of flooding to people, support flood emergency planning and appraise the benefits of flood incident management measures.     

Gradual Deformation and Iterative Calibration of Sequential Stochastic Simulations

Based on the algorithm for gradual deformation of Gaussian stochastic models, we propose, in this paper, an extension of this method to gradually deforming realizations generated by sequential, not necessarily Gaussian, simulation. As in the Gaussian case, gradual deformation of a sequential simulation preserves spatial variability of the stochastic model and yields in general a regular objective function that can be minimized by an efficient optimization algorithm (e.g., a gradient-based algorithm). Furthermore, we discuss the local gradual deformation and the gradual deformation with respect to the structural parameters (mean, variance, and variogram range, etc.) of realizations generated by sequential simulation. Local gradual deformation may significantly improve calibration speed in the case where observations are scattered in different zones of a field. Gradual deformation with respect to structural parameters is necessary when these parameters cannot be inferred a priori and need to be determined using an inverse procedure. A synthetic example inspired from a real oil field is presented to illustrate different aspects of this approach. Results from this case study demonstrate the efficiency of the gradual deformation approach for constraining facies models generated by sequential indicator simulation. They also show the potential applicability of the proposed approach to complex real cases.     

A continuum mechanics based framework for optimizing boundary and finite element meshes associated with underground excavations—accuracy,efficiency and applications

A framework was developed to address the automatic optimization of the level of geometric detail required for stress analysis of underground excavations in mining, which was presented in the companion paper. The motivation for optimizing the mesh geometry stems from the over‐discretization of computational domain as the digital mine model is built while our knowledge of some of the input parameters is quite limited. Thus, the accuracy of the solution is not expected to be increased with a finely discretized mesh, only the computation time does. Therefore, it is acceptable if the results obtained from an optimized model have accuracy comparable to the uncertainty in input data (e.g. rock mass properties, geology, etc.). Although the mesh optimization framework automates the geometry optimization and reduces computation time, the accuracy of the solution from the resulting geometry must be evaluated to ensure the quality of the solution at the ‘region of interest’. Both a priori (mesh quality) and a posteriori (solution quality) measures are employed along with recording the mesh optimization time. Finally, the applicability of the mesh optimization framework is demonstrated by analysing a number of mining and civil engineering underground models. Copyright © 2005 John Wiley & Sons, Ltd.     

A New Differentiable Parameterization Based on Principal Component Analysis for the Low-Dimensional Representation of Complex Geological Models

A new approach based on principal component analysis (PCA) for the representation of complex geological models in terms of a small number of parameters is presented. The basis matrix required by the method is constructed from a set of prior geological realizations generated using a geostatistical algorithm. Unlike standard PCA-based methods, in which the high-dimensional model is constructed from a (small) set of parameters by simply performing a multiplication using the basis matrix, in this method the mapping is formulated as an optimization problem. This enables the inclusion of bound constraints and regularization, which are shown to be useful for capturing highly connected geological features and binary/bimodal (rather than Gaussian) property distributions. The approach, referred to as optimization-based PCA (O-PCA), is applied here mainly for binary-facies systems, in which case the requisite optimization problem is separable and convex. The analytical solution of the optimization problem, as well as the derivative of the model with respect to the parameters, is obtained analytically. It is shown that the O-PCA mapping can also be viewed as a post-processing of the standard PCA model. The O-PCA procedure is applied both to generate new (random) realizations and for gradient-based history matching. For the latter, two- and three-dimensional systems, involving channelized and deltaic-fan geological models, are considered. The O-PCA method is shown to perform very well for these history matching problems, and to provide models that capture the key sand–sand and sand–shale connectivities evident in the true model. Finally, the approach is extended to generate bimodal systems in which the properties of both facies are characterized by Gaussian distributions. MATLAB code with the O-PCA implementation, and examples demonstrating its use are provided online as Supplementary Materials.     

油气运聚过程的综合分析模拟

基于对油气运移机理现有认识的综合研究,提出了油气运聚模拟的地质概念模型和充分考虑地质综合研究并可人工干预的综合分析模拟.大尺度地质概念模型是以含油气系统为单位的三维面状模型,而小尺度模型则是以油气运聚单元为单位的、由一个或多个三维面以及断层组合而成的网络状格架模型.油气运聚过程的综合分析模拟应在层序地层格架内以含油气系统(或油气运聚单元)为单位建立初始地质模型,应用古构造恢复、古流体势恢复、油气运秽路径模拟、优势运移通道分析、模拟结果综合评价等技术,依据层序地层格架所提供的精确时间间隔单元进行三维的、动态的模拟.模拟的相关技术和方法可在信息集成平台的基础上,基于一系列空间数据处理方法开发实现.     

Evaluation of alternative conceptual models for groundwater modelling

This study evaluates the alternative conceptual models for groundwater modelling. A true model was created with a synthetic alluvial fan-plain hydrogeological framework. Various alternative conceptual models were evaluated for groundwater flow simulations. The first alternative model is a single aquifer layer model; the second alternative model is a 3-layer aquifer model; and the third model is a 5-layer model consisting of 3 aquifers separated by 2 aquitards. All models could fit very well to the observations with optimized values of hydraulic conductivities. However, the single aquifer layer model can only compute water balance components with good accuracy. The 3-layer aquifer model can be used for water balance computation and groundwater head simulation with small errors. The 5-layer model is capable of simulating water budget, groundwater head distribution and travel times with high accuracy. Multi-model analysis found only the 3rd alternative model superior.     

一维-二维耦合的河湖系统整体水动力模型

为对自然界中的复杂水流系统进行整体水动力数值模拟,建立了适用于河湖系统的一维-二维耦合模型.将河网计算中用于处理河段间耦合的汊点水位预测校正法应用到一维-二维耦合边界的处理中,即耦合边界水位预测校正法.该方法具有一维、二维模型计算完全独立,可有效利用现有模型的优点.与传统一维-二维耦合处理方法相比,耦合连接条件的满足程度较高且可通过对计算容差的调整进行有效控制.利用理想及实际例子进行了计算验证,结果表明该方法具有较好的实用性.     

频率域反射波全波形速度反演

全波形反演不仅利用相位和振幅信息,还利用波形的细节变化,具有刻画模型精确细节的能力.在对稀疏矩阵直接LU分解求解的基础上,采用梯度预处理方法对声波介质速度模型进行了反射波全波形反演.采用误差反向传播算法计算目标函数梯度以及伪Hessian矩阵的对角线元素来做梯度预处理.数值模型的实验结果表明,利用有效的频率段便能反演出分辨率较高的速度结构,用低频反演出的结果作为高频反演的初始模型,减少了解的非唯一性.二维高斯光滑初始模型提供了有利的低频信息,得到较好的反演结果.伪Hessian矩阵的预处理吸收了高斯牛顿法的二次收敛优势,在不增加计算量的前提下,加快收敛速度.      

Comparison of surrogate models with different methods in groundwater remediation process

Surrogate modelling is an effective tool for reducing computational burden of simulation optimization. In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging methods were compared for building surrogate models of a multiphase flow simulation model in a simplified nitrobenzene contaminated aquifer remediation problem. In the model accuracy analysis process, a 10-fold cross validation method was adopted to evaluate the approximation accuracy of the three surrogate models. The results demonstrated that: RBFANN surrogate model and kriging surrogate model had acceptable approximation accuracy, and further that kriging model’s approximation accuracy was slightly higher than RBFANN model. However, the PR model demonstrated unacceptably poor approximation accuracy. Therefore, the RBFANN and kriging surrogates were selected and used in the optimization process to identify the most cost-effective remediation strategy at a nitrobenzene-contaminated site. The optimal remediation costs obtained with the two surrogate-based optimization models were similar, and had similar computational burden. These two surrogate-based optimization models are efficient tools for optimal groundwater remediation strategy identification.     

Determination of lower and upper bounds of predicted production from history-matched models

We present a method to determine lower and upper bounds to the predicted production or any other economic objective from history-matched reservoir models. The method consists of two steps: 1) performing a traditional computer-assisted history match of a reservoir model with the objective to minimize the mismatch between predicted and observed production data through adjusting the grid block permeability values of the model. 2) performing two optimization exercises to minimize and maximize an economic objective over the remaining field life, for a fixed production strategy, by manipulating the same grid block permeabilities, however without significantly changing the mismatch obtained under step 1. This is accomplished through a hierarchical optimization procedure that limits the solution space of a secondary optimization problem to the (approximate) null space of the primary optimization problem. We applied this procedure to two different reservoir models. We performed a history match based on synthetic data, starting from a uniform prior and using a gradient-based minimization procedure. After history matching, minimization and maximization of the net present value (NPV), using a fixed control strategy, were executed as secondary optimization problems by changing the model parameters while staying close to the null space of the primary optimization problem. In other words, we optimized the secondary objective functions, while requiring that optimality of the primary objective (a good history match) was preserved. This method therefore provides a way to quantify the economic consequences of the well-known problem that history matching is a strongly ill-posed problem. We also investigated how this method can be used as a means to assess the cost-effectiveness of acquiring different data types to reduce the uncertainty in the expected NPV.     

A synthetic study of geophysics-based modelling of groundwater flow in catchments with a buried valley

Groundwater models simulating flow in buried valleys interacting with regional aquifers are often based on hydrogeological models interpreted from dense geophysical datasets and scarce borehole data. For three simple synthetic cases, it is demonstrated that alternative methods of inversion of transient electro-magnetic (TEM) data can lead to very different interpretations of the hydrogeology inside and surrounding a buried valley. The alternative interpreted hydrogeological models are used in numerical modelling of groundwater flow to a pumping well. It is demonstrated that the alternative models result in quite different groundwater-model predictions of capture zone, recharge area, and groundwater age for the pumping well. It is briefly demonstrated that model calibration against hydraulic head data is not likely to improve the predictions or to identify the structural error of the interpreted hydrogeological models. It is therefore concluded that when TEM-based resistivity models are interpreted to construct the hydrogeological framework of a groundwater model, it must be done cautiously with support from deep borehole information. Too much reliance on geophysical mapping can lead to seriously wrong hydrogeological models and correspondingly wrong groundwater-model predictions.     

一种土的非线性弹性本构模型参数的反演方法

旨在提出一种土的非线性弹性本构模型参数反演的方法。以现今普遍实行的地基载荷试验为基础,依据遗传算法的组合优化理论,采用正演计算和遗传算法优化相结合的方式,建立了土层非线性弹性本构模型参数反演的方法;并依据某黄土场地地基载荷试验数据,实施了黄土土层非线性弹性本构模型参数反演的全过程。计算结果表明,所建立的方法可以实现土层非线性弹性本构模型中相互关联的多个参数的组合优化,并在对初始值要求较低的情况下,可以获得良好的参数反演精度。从而为土的变形特性分析和土与其中及相邻结构的共同作用分析,提供了较好的土体本构模型参数的确定方法。     

Data Assimilation in Truncated Plurigaussian Models: Impact of the Truncation Map

Assimilation of production data into reservoir models for which the distribution of porosity and permeability is largely controlled by facies has become increasingly common. When the locations of the facies bodies must be conditioned to observations, the truncated plurigaussian model has been often shown to be a useful method for modeling as it allows gaussian variables to be updated instead of facies types. Previous experience has also shown that ensemble Kalman filter-like methods are particularly effective for assimilation of data into truncated plurigaussian models. In this paper, some limitations are shown of the ensemble-based or gradient-based methods when applied to truncated plurigaussian models of a certain type that is likely to occur for modeling channel facies. It is also shown that it is possible to improve the data match and increase the ensemble spread by modifying the updating step using an approximate derivative of the truncation map.     

Conditioning Surface-Based Geological Models to Well and Thickness Data

Geostatistical simulation methods aim to represent spatial uncertainty through realizations that reflect a certain geological concept by means of a spatial continuity model. Most common spatial continuity models are either variogram, training image, or Boolean based. In this paper, a more recent spatial model of geological continuity is developed, termed the event, or surface-based model, which is specifically applicable to modeling cases with complex stratigraphy, such as in sedimentary systems. These methods rely on a rule-based stacking of events, which are mathematically represented by two-dimensional thickness variations over the domain, where positive thickness is associated with deposition and negative thickness with erosion. Although it has been demonstrated that the surface-based models accurately represent the geological variation present in complex layered systems, they are more difficult to constrain to hard and soft data as is typically required of practical geostatistical techniques. In this paper, we develop a practical methodology for constraining such models to hard data from wells and thickness data interpreted from geophysics, such as seismic data. Our iterative methodology relies on a decomposition of the parameter optimization problem into smaller, manageable problems that are solved sequentially. We demonstrate this method on a real case study of a turbidite sedimentary basin.     

Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow

An adjoint formulation for the gradient-based optimization of oil–gas compositional reservoir simulation problems is presented. The method is implemented within an automatic differentiation-based compositional flow simulator (Stanford’s Automatic Differentiation-based General Purpose Research Simulator, AD-GPRS). The development of adjoint procedures for general compositional problems is much more challenging than for oil–water problems due to the increased complexity of the code and the underlying physics. The treatment of nonlinear constraints, an example of which is a maximum gas rate specification in injection or production wells, when the control variables are well bottom-hole pressures, poses a particular challenge. Two approaches for handling these constraints are presented—a formal treatment within the optimizer and a simpler heuristic treatment in the forward model. The relationship between discrete and continuous adjoint formulations is also elucidated. Results for four example cases of increasing complexity are presented. Improvements in the objective function (cumulative oil produced) relative to reference solutions range from 4.2 to 11.6 %. The heuristic treatment of nonlinear constraints is shown to offer a cost-effective means for obtaining feasible solutions, which are, in some cases, better than those obtained using the formal constraint handling procedure.