Comparison of model-based generalized regression neural network and prestack inversion in predicting Poisson's ratio in Heidrun Field, North Sea

Author:

Keynejad Saba1,Sbar Marc L.1,Johnson Roy A.1

Affiliation:

1. University of Arizona, Department of Geosciences.

Abstract

Drilling wells in the oil and gas industry is a substantial process, whether they are appraisal wells drilled for reservoir-characteristic assessments at the exploration stage or production wells drilled following prior assessments. The challenge has always been to reduce drilling-related expenses and natural/environmental hazards by reducing the number of wells drilled, and to evaluate reservoir characteristics with as few calibration wells as possible. Physical and mathematical modeling of seismic data can help us understand the geologic and structural formations with minimal wells, and interpolate reservoir characteristics across large areas between a few drilled wells. In a new comparative approach, simultaneous prestack inversion and artificial neural network (ANN) methods are used to create 3D Poisson's ratio (PR) models built upon low-frequency initial models (IMs). Training the ANN on IMs similar to those used in the inversion has improved its performance while creating a valid base of comparison between the two methods. The inversion method was able to model the PR around four wells that had been used in creating the IMs. The generalized regression neural network that was trained on a PR IM, along with other seismic attributes, gave results that were consistent with the existing wells. The results of both methods confirm the existence of a strong relationship between PR and known hydrocarbon presence in these wells. However, examining the results with a blind well showed that the ANN was notably more successful than inversion in extrapolating the results beyond the logged sections in the wells and away from control wells. While this particular conclusion cannot be generalized, and the results obtained from the same methodology may vary from one reservoir to another, such results suggest that this procedure can become a robust part of a predrilling reservoir-evaluation phase in developing hydrocarbon fields.

Publisher

Society of Exploration Geophysicists

Subject

Geology,Geophysics

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3