Unlocking Field Potential of Mature Fields Using Hybrid Fuzzy Modelling and Kriging Method

Saransh Surana
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Abstract

Reservoir uncertainties, high water cut, completion integrity along with declining production are the major challenges of a mature field. These integrated with dying facilities and poor field production are key issues that each oil and gas company is facing these days. Arresting production decline is an inevitable objective, but with the existing techniques/steps involved, it becomes a cumbersome and exorbitant affair for the operators to meet their requirements. In addition, incompetent and flawed well data makes it more challenging to analyze mature fields. Although flow rate data is the most easily accessible data for mature fields, the absence of pressure data (flowing bottom-hole or wellhead pressure) remains a big obstacle for the application of conventional production enhancement and well screening strategies for most of the mature fields. A real-time optimization tool is thus constructed by developing a hybrid modelling technique that encapsulates Kriging and Fuzzy Logic to account for the imprecisions and uncertainties involved while identification of subsurface locations for production optimization of a mature field using only production data. The data from the existing wells in the field is used to generate a membership function based on its historical performance and productivity, thereby generating a spatial map of prospective areas, where secondary development operations can be taken up for production optimization.
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利用混合模糊建模和Kriging方法解锁成熟油田的场势
油藏不确定性、高含水、完井完整性以及产量下降是成熟油田面临的主要挑战。这些问题与设备老化和油田产量低相结合,是当今每个油气公司面临的关键问题。遏制产量下降是一个不可避免的目标,但由于现有的技术/步骤,对于运营商来说,要满足他们的要求是一件繁琐而昂贵的事情。此外,不合格和有缺陷的井数据使分析成熟油田更具挑战性。虽然流量数据是成熟油田最容易获得的数据,但缺乏压力数据(井底或井口流动压力)仍然是大多数成熟油田常规增产和筛井策略应用的一大障碍。因此,通过开发一种混合建模技术来构建实时优化工具,该技术封装了Kriging和模糊逻辑,以解释在仅使用生产数据识别成熟油田地下位置以优化生产时所涉及的不精确和不确定性。该油田现有井的数据用于根据其历史表现和生产力生成隶属函数,从而生成潜在区域的空间图,从而可以采取二次开发作业来优化生产。
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