储层描述

机器学习方法优化复杂油藏的地层压力测试

本文介绍了一种机器学习方法，该方法整合了测井数据来增强深度选择，从而增加了获得准确、有价值的地层压力结果的可能性。

图 1 的混淆矩阵说明了 ANN 模型在预测有效和无效测试结果方面的表现，其中深色表示分类频率较高。
图 1 的混淆矩阵说明了 ANN 模型在预测有效和无效测试结果方面的表现，其中深色表示分类频率较高。
来源：SPE 224365。

传统上，地层压力测试 (FPT) 深度选择在很大程度上依赖于工程师和地球科学家通过分析测井数据来确定测试位置的专业知识。手动方法可能存在不一致、耗时且容易受到人为偏见的影响。为了应对这些挑战，本研究引入了一个机器学习 (ML) 框架来增强 FPT 深度选择，系统地改进基于测井数据的决策。该框架旨在实现一致可靠的测试位置，最大限度地减少无效测试，提高安全性并降低运营成本。

脳
SPE徽标CMYK反式
成为 SPE 会员继续阅读
SPE 会员：请在页面顶部登录，即可访问此会员专属内容。如果您还不是会员，但觉得 JPT 内容很有价值，我们鼓励您加入 SPE 会员社区，获取完整访问权限。
加入SPE
原文链接/JPT
Reservoir characterization

Machine-Learning Approach Optimizes  Formation-Pressure Testing in Complex Reservoirs

This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.

Fig. 1—Confusion matrix illustrates the ANN model’s performance in predicting valid and invalid test outcomes where darker shades indicate higher classification frequency.
Fig. 1—Confusion matrix illustrates the ANN model’s performance in predicting valid and invalid test outcomes where darker shades indicate higher classification frequency.
Source: SPE 224365.

Traditionally, formation-pressure-test (FPT) depth selection relies heavily on the expertise of engineers and geoscientists in analyzing well-logging data to determine test locations. A manual approach can be inconsistent, time-consuming, and prone to human bias. To address these challenges, this study introduces a machine-learning (ML) framework to enhance FPT depth selection, systematically improving decision-making based on well-log data. The proposed framework aims for consistent and reliable test placement, minimizing invalid tests, enhancing safety, and reducing operational costs.

×
SPE_logo_CMYK_trans_sm.png
Continue Reading with SPE Membership
SPE Members: Please sign in at the top of the page for access to this member-exclusive content. If you are not a member and you find JPT content valuable, we encourage you to become a part of the SPE member community to gain full access.
Join SPE