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首页> 外文期刊>Journal of Petroleum Science & Engineering >Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field,Algeria
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Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field,Algeria

机译:人工智能在阿尔及利亚Hassi Messaoud油田表征天然裂缝带中的应用

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In highly heterogeneous reservoirs classical characterization methods often fail to detect the location and orientation of the fractures.Recent applications of Artificial Intelligence to the area of reservoir characterization have made this challenge a possible practice.Such a practice consists of seeking the complex relationship between the fracture index and some geological and geomechanical drivers (facies,porosity,permeability,bed thickness,proximity to faults,slopes and curvatures of the structure) in order to obtain a fracture intensity map using Fuzzy Logic and Neural Network. This paper shows the successful application of Artificial Intelligence tools such as Artificial Neural Network and Fuzzy Logic to characterize naturally fractured reservoirs.A 2D fracture intensity map and fracture network map in a large block of Hassi Messaoud field have been developed using Artificial Neural Network and Fuzzy Logic. This was achieved by first building the geological model of the permeability,porosity and shale volume using stochastic conditional simulation.Then by applying some geomechanical concepts first and second structure directional derivatives,distance to the nearest fault,and bed thickness were calculated throughout the entire area of interest.Two methods were then used to select the appropriate fracture intensity index.In the first method well performance was used as a fracture index.In the second method a Fuzzy Inference System (FIS) was built.Using this FIS,static and dynamic data were coupled to reduce the uncertainty,which resulted in a more reliable Fracture Index.The different geological and geomechanical drivers were ranked with the corresponding fracture index for both methods using a Fuzzy Ranking algorithm.Only important and measurable data were selected to be mapped with the appropriate fracture index using a feed forward Back Propagation Neural Network (BPNN).The neural network was then used to obtain a fracture intensity maps throughout the entire area of interest.A mathematical model based on "the weighting method" was then applied to obtain fracture network maps,which led to a better description of the major fracture trends. The obtained maps were compared and the results show that the proposed approach is a feasible and practical methodology to map the fracture network.
机译:在高度异构的油藏中,经典的表征方法通常无法检测裂缝的位置和方向。人工智能在储层表征领域的最新应用使这一挑战成为可能的实践。这种实践包括寻找裂缝之间的复杂关系。指数和一些地质和地质力学驱动因素(相,孔隙度,渗透率,床层厚度,与断层的邻近度,结构的坡度和曲率),以便使用模糊逻辑和神经网络获得裂缝强度图。本文展示了人工神经网络和模糊逻辑等人工智能工具在自然裂缝储层特征描述中的成功应用。利用人工神经网络和模糊算法开发了哈斯马萨乌德大块油田的二维裂缝强度图和裂缝网络图。逻辑。这是通过首先使用随机条件模拟建立渗透率,孔隙度和页岩体积的地质模型来实现的,然后通过应用一些地质力学概念来确定一,二阶结构的方向导数,到最近断层的距离以及整个区域的床层厚度然后使用两种方法选择合适的裂缝强度指标。第一种方法将良好的性能作为裂缝指标。第二种方法建立了模糊推理系统(FIS)。数据耦合以减少不确定性,从而产生更可靠的断裂指数。使用模糊评级算法对两种方法的不同地质和地质力学驱动因素和相应的断裂指数进行排序,仅选择重要且可测量的数据进行映射使用前馈反向传播神经网络(BPNN)来确定合适的骨折指数。然后,使用k来获得整个感兴趣区域的裂缝强度图。然后,基于“加权方法”的数学模型被应用于获得裂缝网络图,从而更好地描述了主要裂缝趋势。对获得的图进行比较,结果表明,该方法是绘制裂缝网络的可行方法。

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