标题:
地质分层自动解释和储层物性预测-在加拿大西部沉积盆地十万口井大数据工区中的应用
作者:
张宝森,叶天睿,肖倚天,姚同云等
内容:
精确的地质分层自动解释和储层物性预测对油气勘探开发具有非常重要的意义,这项工作传统上是由地质工程师手工对比完成,耗时且低效,由于个人认识的差异,可能导致出现多个不同的地质分层方案,整个地层对比工作难以标准化和流程化,尤其对于拥有数万口井的成熟开发区块,构建精确的地质模型成为一项艰巨的任务。目前机器学习作为一项新兴技术,在石油行业得到了广泛的应用。
Transform软件基于机器学习的地质分层自动解释技术,它可以提供自动化、精确的地质分层自动拾取,本文以加拿大西部沉积盆地的Belly River组为例,探讨了在十万口井的大数据工区地质分层自动解释和储层物性快速评价的可行性。在海量数据清理的基础上,利用Subsequen Dynamic Time Warping 动态时间规整的机器学习算法,充分考虑了相邻测井曲线拉伸、挤压和移位组合等多种情况,来捕捉地层之间的横向变化。本文为国内外地质学家将大数据-机器学习技术应用于实际工区的地质分层自动解释给出了一个成功的示范案例。
Automated Well Top Picking and Reservoir Property Analysis of the Belly River Formation of the Western Canada Sedimentary Basin
Baosen Zhang*1, Tianrui Ye1, Yitian Xiao1, Dongmei Li2, Guoping Wang2, Cong Su2, Tongyun Yao3,
1. Petroleum Exploration and Production Research Institute, SINOPEC, 2. International Petroleum Exploration and Production Corporation, SINOPEC, 3. ESSCA Group
Copyright 2022, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2022-3719133
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Houston, Texas, USA, 20-22 June 2022.
The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper by anyone other than the author without the written consent of URTeC is prohibited.
Abstract
Accurate well top picking and reservoir property prediction plays crucial role for petroleum exploration and production. The task is traditionally done manually by geologists, and can be inconsistent and time-consuming. In addition, the work is difficult to normalize and standardize due to human bias. For unconventional resource plays with tens of thousands of wells, constructing geological models and algorithms can be a daunting task. Machine learning is an emerging technology that has been increasingly adopted in the energy industry. It can provide automated and accurate well top picking and reservoir property analysis.
This paper utilizes a case study in the Belly River Formation (BRF) of Western Canada Sedimentary Basin (WCSB) to discuss capabilities EnABLEd by automated well top picking and reservoir property analysis. First, 70993 wells with GR curve covering ~100000 km2of WCSB were filtered. Among them, 32510 coring wells help to determine boundaries of BRF. Second, several tops were manually picked as seeds for automated picking using Subsequent Dynamic Time Warping approach. After quality control and log normalization, automatic picks were promoted into new seeds for subsequent picking until all pickings were done. Finally, the distribution of the BRF were defined, and combining with logging curves, the variation law of reservoir properties (porosity, permeability, saturation, etc.) was analyzed.
Automated well top picking algorithm natively handles log normalization issues and picks. It completed ~70000s wells top picks in about 100 hours on cross section and map view, which may take over 1000 hours using traditional manual picking methods. Moreover, after automated well top picking, reservoir properties can be predicted as a “one-mouse-click” exercise. What need to do is to ascertain the acquired reservoir properties according to the production practice and to determine the algorithms and formulas according to the regional geological features. This workflow greatly improves efficiencies of the comprehensive reservoir evaluation and reservoir geological modeling of the WCSB by orders of magnitude.
Subsequently, combining automated well top picking and reservoir property analysis results and real-time data of oilfield production, the exploration and production sweet spot prediction of the BRF of the WCSB can be done. In conclusion, this efficient approach based on machine learning has been successfully applied to the potential assessment of petroleum resources in the BRF. The assessment results were used for petroleum reservoir exploration and production, oilfield development plan design, and portfolio management and optimization. Application of the method requires cooperation across different disciplines—data science and earth science. The interdisciplinary nature provides accurate prediction and design optimization for unconventional resources exploration and production.
Introduction
Since the 21st century, large-scale computing, big data and deep learning have triggered the Third AI Boom (Brynjolfsson and Mitchell, 2017). Recently, AI has also been widely used in petroleum exploration and production. Operators cooperate with academic institutes, IT companies and vendors to carry out AI application research, which is developing rapidly in the direction of digitalization, integration, visualization and artificial intelligence application (Li et al., 2020).
Accurate well top picking and reservoir property prediction plays crucial role for petroleum exploration and production. The task is traditionally done manually by geologists and can be inconsistent and time-consuming. In addition, the work is difficult to normalize and standardize due to human bias. For unconventional resource plays with tens of thousands of wells, constructing geological models and algorithms can be a daunting task. Machine learning is an emerging technology that has been increasingly adopted in the energy industry. It can provide automated and accurate well top picking and reservoir property analysis.
Previous attempts have been made to pick geologic well tops automatically using expert systems (Olea, 2003), neural networks (Luthi, 2001), and dynamic programming (Lineman et al., 1987; Inazaki, 1994; Steven et al., 2004; Fang, 2009). Although these previous efforts have been helpful in defining the problems and establishing the building blocks to solve well-log correlation automatically, owing to the nature of seismic data, they have clearly been observed to be much less successful than seismic picking algorithms. Comparing to seismic traces, well logs are more widely spaced (on the order of hundreds to thousands of meters), have inconsistent depth ranges with possible gaps, and may be from highly non-vertical well bores. As a variant of the Dynamic Time Warping (DTW) algorithm, Subsequence Dynamic Time Warping (SDTW) was introduced by Grant et al. (2018) to perform the relevant curve alignments. This technology and workflow uses the power of the modern computer and novel machine learning techniques to capture and model well-log patterns for correlating geologic events across thousands of wells. Using one or more well logs as source wells, a signature ‘thumbprint’ segment is correlated over many target wells to find the optimal stratigraphic intervals for well pick estimation.
This paper utilizes a case study in the BRF of the WCSB to discuss the capabilities enabled by automated well top picking and reservoir property analysis. This study is implemented to support the project of “Western Canada Sedimentary Basin– Edmonton/Belly River Potential Analysis” from SINOPEC International Petroleum Exploration and Production Corporation. First, an overview of the geological setting of the BRF of the WCSB was provided. Second, the methodology of the automated well top picking, including the theoretical basis and workflow, was explained in detail. Third, reservoir property analysis is applied to the BRF of the WCSB based on the automated well top picking results, empirical formulas or machine learning algorithms for property inference, and evaluations by geologists and engineers of petroleum exploration and production. Finally, to further deepen this research results, in the future the production sweet spot prediction model will be established based on production data and reservoir property analysis results, and the intelligent prediction of petroleum favorable areas in the BRF of the WCSB will be completed (Figure 1).
Figure 1. Flowchart summarizing the workflow of automated well top picking and reservoir property analysis of the BRF of the WCSB in this paper. It begins with automated target well top picking, followed by reservoir property analysis. Then the production sweet spot prediction is a future plan based on the above two works. Finally, a scientific and efficient data analysis and machine learning workflow will be developed.