现场/项目开发

机器学习有助于定制间距以获得最大面积价值

在本文中，使用地质、完井和间距参数对示例机器学习模型进行了训练，以预测米德兰盆地内主要发育地层的产量。

地球化学分型数据之前和之后的排水高度定义。
图1'总结了地球化学分型数据前后的降雨高度定义。
URTeC 3723023。

在完整的论文中，机器学习 (ML) 模型使用地质、完井和间距参数进行训练，以预测米德兰盆地主要发育地层的产量。使用机器学习测试间距和完井设计的几种不同组合的方法可以在整个盆地中重复，以便为每个开发单元找到经济的定制解决方案。

介绍

与传统方法相比，机器学习提供了一种数据驱动的方法，可以利用运营商在非常规游戏中生成的大量数据。机器学习模型的几个特征使其非常适合间距优化，包括以下特征：

  • 机器学习模型依靠统计方法来建立输入变量和输出变量之间的关系。
  • 机器学习可以很好地处理非线性关系。
  • 使用传统方法可能难以理解复杂的变量相互作用。

缺点是组装用于训练机器学习模型的输入数据需要时间。然而，一旦建立了具有可接受的错误水平的模型以及涵盖要评估的案例范围的数据示例，就可以快速评估许多替代开发场景。

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Field/project development

Machine Learning Helps Customize Spacing for Maximum Acreage Value

In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.

Drainage height definitions before and after geochemical typing data.
Fig. 1—Drainage height definitions before and after geochemical typing data.
URTeC 3723023.

In the complete paper, machine-learning (ML) models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations in the Midland Basin. The approach of using ML to test several different combinations of spacing and completion designs can be repeated across a basin to find an economical, customized solution for each development unit.

Introduction

In contrast to conventional methods, ML offers a data-driven approach that can leverage the large amount of data generated by operators within unconventional plays. Several characteristics of ML models make them well-suited for spacing optimization, including the following:

  • ML models rely on statistical methods to establish relationships between the input variables and the output variables.
  • ML handles nonlinear relationships well.
  • Complex variable interactions can be difficult to understand with traditional methods.

The downside is the time required to assemble the input data used for training an ML model. However, once a model is built that has an acceptable level of error along with data examples covering the range of cases to be evaluated, many alternative development scenarios can be quickly evaluated.

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