现场/项目开发

集成机器学习、数值模拟来估计儿童健康损耗

本文的作者结合可用的经验数据和数值模拟输出来分析稳健、分布均匀的母井/子井数据集,以开发预测机器学习模型。

与母井相关的子井类型分类。 类型 1:子井在两个母井之间完井。 类型 2:子井与母井相邻,同时完成加密井。 类型 3:子井在单亲井附近完成。
图1-子井类型相对于母井的分类。类型 1:子井在两个母井之间完井。类型 2:子井与母井相邻,同时完成加密井。类型 3:子井在单亲井附近完成。
URTeC 3719366。

在完整的论文中,作者结合可用的经验数据和数值模拟输出,分析了特拉华盆地 Wolfcamp 地层的稳健、分布均匀的母井/子井数据集,该数据集用于开发预测机器学习模型(由多元线性回归模型和简单的神经网络组成)。该模型已在现场开发中成功实施,以优化子井安置,并改进了性能预测和净现值。

介绍

普遍存在的母井/子井对引入了可靠预测子井性能的需求,从而使特拉华盆地 Wolfcamp 地层的开发变得复杂。由于母井/子井相互作用所涉及的物理过程的复杂性以及可以实现的几何配置的多样性,使这个问题变得更加困难。从广义上讲,根据子井与相关母井和其他偏置井的空间关系,可以将子井分为以下三类(上图1)

  • 1 型——在两个母井之间完成的子井
  • 2 类——在母井附近完成的子井以及同时完成或共同开发的加密井
  • 类型 3——在单母井附近完成的子井

为了缩小研究的复杂性范围,作者将重点放在 2 型子井上,因为这种配置将在未来的开发活动中最常用,而且它拥有最多的现有现场示例。

该评估的主要目标是对由于预先存在的母井而造成的子井生产性能下降进行准确的定量预测。

在这项工作中,详细介绍了一种新颖的混合方法,涉及机器学习技术和数值模拟的结合。

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原文链接/jpt
Field/project development

Machine Learning, Numerical Simulation Integrated To Estimate Child-Well Depletion

The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.

Classification of child-well types in relation to parent wells. Type 1: Child well completed in between two parent wells. Type 2: Child well completed adjacent to a parent well along with concurrently completed infill wells. Type 3: Child well completed adjacent to a single parent well.
Fig. 1—Classification of child-well types in relation to parent wells. Type 1: Child well completed in between two parent wells. Type 2: Child well completed adjacent to a parent well along with concurrently completed infill wells. Type 3: Child well completed adjacent to a single parent well.
URTeC 3719366.

In the complete paper, the authors analyzed a robust, well-distributed parent/child well data set of the Delaware Basin Wolfcamp formation using a combination of available empirical data and numerical simulation outputs, which was used to develop a predictive machine-learning model (consisting of a multiple linear regression model and a simple neural network). This model has been implemented successfully in field developments to optimize child-well placement and has enabled improvements in performance predictions and net present value.

Introduction

Pervasive parent/child well pairs have complicated the development of the Delaware Basin Wolfcamp formation by introducing the need to forecast child-well performance reliably. This problem is made more difficult by the complex nature of the physical processes involved in parent/child well interactions and the variety of geometrical configurations that can be realized. In broad terms, the following three classifications of child wells can be recognized based on their spatial relationship to the associated parent well and other offset wells (Fig. 1 above):

  • Type 1—Child wells completed in between two parent wells
  • Type 2—Child wells completed adjacent to a parent well along with concurrently completed or codeveloped infill wells
  • Type 3—Child wells completed adjacent to a single parent well

To narrow the range of complexities in the study, the authors focused on Type 2 child wells because this configuration will be used most often in future development activities and because it had the most existing field examples.

The principal objective of this assessment was to generate accurate quantitative predictions of the diminished production performance of child wells because of pre-existing parent wells.

In this work, a novel, hybrid approach is detailed involving a combination of machine-learning techniques and numerical simulations.

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