Applying Machine Learning Methods to Search for Missing Net Pay Zones in Mature Oilfields Wells

D. Egorov, A. Sabirov, O. Osmonalieva, B. Belozerov, A. A. Reshytko, A. Klenitskiy, A. Shchepetnov, A. Semenikhin
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引用次数: 1

Abstract

The main aim of this work is to develop new approaches for processes of searching previously missed potential net pay intervals within wells from well log data of brownfields with machine learning algorithms. The practical interest for such a solution is an extraction of additional oil potential recovery from field data in order to prolongate development and production life of mature fields through development of system capable to automatically make recommendations on previously misinterpreted intervals. Besides application of cognitive technologies the developed methodology includes also a workflow of cross-functional interaction of experts from different disciplines (geologist, petrophysicists) between each other and digital system. The system is based on modern deep learning architectures including convolutional and recurrent artificial neural networks. It utilizes well log data and corresponding interpretation of many specialists in order to accumulate or digitize a vast amount of previous experience. It can be used for the following robust re-interpretation of data in those parts of wells which were not previously considered in terms of net pay intervals but have high potential for new oil saturated thicknesses according to geological conditions and previous experience of manual investigation of such intervals. The field test on a basis of described methodology was conducted on assets of Gazpromneft PJSC in Yamanlo-Nenets Autonomous Region, Western Siberia. The comprehensive volume of geological and geophysical information from oilfield was collected including well log data and corresponding results of expert interpretation. This information is used for model training and then predictions about previously uninterpreted intervals are made, providing business user a new interpretation of target geological objects. New interpretation produced by model was compared with current manual interpretation and new net pay intervals were considered as previously missed and potentially oil saturated. At the next step those intervals were examined by petrophysicists, geologists and reservoir engineers in order to estimate probability of oil saturation. Intervals with highest expert marks were proposed for field work and tested by perforation of the target zone. As a result of described process new net pay intervals were found and well, which was suspended, started a new production life. Obtained results confirm high potential of machine learning models application for search of new potential net pay intervals by helping an expert in daily geological and petrophysical tasks.
应用机器学习方法寻找成熟油田缺失的净产层
这项工作的主要目的是开发新的方法,利用机器学习算法从棕地的测井数据中搜索以前错过的井内潜在的净产层。这种解决方案的实际目的是从现场数据中提取额外的石油采收率,从而通过开发能够自动对以前被误解的层段提出建议的系统来延长成熟油田的开发和生产寿命。除了认知技术的应用,开发的方法论还包括来自不同学科(地质学家、岩石物理学家)的专家相互之间和数字系统之间的跨职能互动的工作流程。该系统基于现代深度学习架构,包括卷积和循环人工神经网络。它利用测井数据和许多专家的相应解释,以积累或数字化大量以前的经验。根据地质条件和以往人工调查这类井段的经验,该方法可用于对以前未考虑到净产层,但具有高潜在新饱和油厚度的井段进行数据重新解释。基于上述方法的现场测试是在西伯利亚西部亚马洛-涅涅茨自治区Gazpromneft PJSC的资产上进行的。收集了油田的综合地质和地球物理信息,包括测井资料和相应的专家解释结果。该信息用于模型训练,然后对先前未解释的区间进行预测,为业务用户提供目标地质对象的新解释。将模型产生的新解释与目前的人工解释进行比较,并将新的净产层视为以前错过的和潜在的油饱和层。下一步,由岩石物理学家、地质学家和油藏工程师对这些层段进行检查,以估计含油饱和度的概率。专家评分最高的井段被建议用于现场工作,并通过靶区的射孔测试。由于上述工艺,发现了新的净产层,暂停的井开始了新的生产周期。通过帮助专家完成日常地质和岩石物理任务,获得的结果证实了机器学习模型应用于寻找新的潜在净产层的巨大潜力。
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