基于混合参数化和优化方法的历史匹配

Basil Al-shamma, O. Gosselin, P. King
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引用次数: 1

摘要

储层模型在油气行业中常用来预测储层动态和预测产量,从而做出重要的财务决策,如储量估计、填充井钻井、提高采收率方案等。将油藏模型调整为动态生产数据被称为历史拟合,通常是为了提高预测的油藏动态。不确定性量化也是这项任务的一个重要方面,包括识别多个历史匹配模型,这些模型受地质概念的限制。历史匹配和不确定性量化可以通过识别和使用高效和快速的优化技术来完成。辅助历史匹配实践通常包括两个实践;首先是参数化,它包括减少匹配参数的数量,以避免根据可用的生产数据量调整太多变量。除了逆问题的不适定公式外,过度参数化还会导致一个具有挑战性的情况。第二个过程涉及优化,其目的是通过减少定义模拟数据与生产数据之间差异的不拟合或目标函数来解决反问题。优化的主要挑战是局部最小解和过早收敛。优化的成功很大程度上取决于所使用的参数化策略。这些算法分析了各种参数化方法,并与各种优化算法相结合和检验,使我们提出了解决辅助历史匹配两个过程的新型混合方法。提出了一种多阶段组合参数化和优化历史匹配技术。当以最优方式设计参数化和优化算法时,混合可以结合每种方法的优势特征。这包括将早期通过宽参数搜索空间优化器随机初始参数填充与基于初始参数分布从前阶段的最佳历史匹配模型中选择的初始模型与后期的微调优化算法相结合。混合算法在每个阶段开始时的重新参数化有助于避免局部最小值,防止过早收敛。这些算法的总体设计是采用简单的参数化方法和广泛的搜索算法进行初始化,随着时间的推移,参数化分区增大,参数搜索空间减小。这些混合算法允许一致和有效的参数搜索空间定义,其中可以达到多个最小值,在达到初始收敛后进一步减少不匹配,通过加速优化过程提高效率,从而节省宝贵的计算时间,从而实现改进的结果。我们还表明,当与布鲁日合成模型进行基准测试时,这些混合算法可以成为具有改进的可预测性模型的不确定性范围的基础。在三阶段混合算法的情况下,与第一次猜测模型相比,某些情况下的错拟合减少可以提高高达50%,而具有停止准则的混合算法的效率提高对于Brugge模型等小型模型节省高达8小时,对于具有多达50,000个活动网格单元的大型模型节省约100小时。最后,针对类似情况,提出了一种推荐的混合算法设计。我们还证明了结果与用于历史匹配的第一猜测模型无关,并与布鲁日基准模型进行了分析。混合方法提供了一种新的技术,该技术结合了有效的参数化,定义了最优参数搜索空间,同时不影响失配最小化的有效性,从而提高了预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
History Matching Using Hybrid Parameterisation and Optimisation Methods
Reservoir models are commonly used in the oil and gas industry to predict reservoir behaviour and forecast production in order to make important financial decision such as reserves estimations, infill well drilling, enhanced oil recovery schemes, etc. Conditioning reservoir models to dynamic production data is known as history matching, which is usually carried out in an attempt to enhance the predicted reservoir performance. Uncertainty quantification is also an important aspect of this task, and encompasses identifying multiple history matched models, which are constrained to a geological concept. History matching and uncertainty quantification can be accomplished by identifying and using efficient and speedy optimisation techniques. The assisted history matching practice usually includes two practices; the first of which is parameterisation, which consists of reducing the number of matching parameters in order to avoid adjusting too many variables with respect to the amount of production data available. A challenging situation results from over-parameterisation, in addition to an ill-posed formulation of the inverse problem. The second process involves optimisation, which aims at solving the inverse problem by reducing a misfit or objective function that defines the difference between simulated and production data. The main challenges of optimisation are local minima solutions and premature convergence. The success of optimisation is greatly dependent on the parameterisation strategy used. These algorithms that analyse various parameterisation methods, combined and examined with diverse optimisation algorithms lead us to suggest novel hybrid approaches addressing the two processes of assisted history matching. We propose a multistage combined parameterisation and optimisation history matching technique. Hybridisation of parameterisation and optimisation algorithms when designed in an optimum manner can combine advantageous features of each method. This consisted of combining random initial parameter population by means of a wide parameter search space optimiser at early stages with initial models chosen from the best history matched models of previous stages based on the initial parameter distribution with a fine tuning optimisation algorithm at later stages. The re-parameterisation at the beginning of each stage of a hybrid algorithm assists the process in escaping local minima and prevents premature convergence. The general design of these algorithms is to initialise with simple parameterisation methods and wide spread search algorithms, in which parameterisation zoning is increased and the parameter search space is reduce with time. These hybrid algorithms allow for consistent and effective parameter search space definition in which more than one minimum can be reached, further reduce the misfit after an initial convergence has been reached, improve efficiency by accelerating the optimisation process saving valuable computing time and consequently, improved results are achieved. We also show that these hybrid algorithms can be the basis of an uncertainty range with improved predictability models when benchmarked with the Brugge synthetic model. In the case of a three stage hybrid algorithm, the misfit reduction in some cases can be improved by up to 50% relative to the first guess model, while the efficiency improvement of a hybrid algorithm with a stopping criterion saves up to eight hours for small models such as the Brugge model and an estimated 100 hours for larger models with up to 50,000 active gridcells. Finally, a recommended hybrid algorithm design for similar cases is established. We also prove that the results are independent of the first guess models used for history matching when analysed with the Brugge benchmark model. The hybrid methods offer a novel technique that incorporates effective parameterisation which defines an optimal parameter search space, and at the same time does not compromise the effectiveness of the misfit minimisation which leads to better predictive capabilities.
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