A Novel Shale Well Production Forecast Model Achieves >95% Accuracy Using Only 1.5 Years of Production Data

Author:

Haider Syed1,Saputra Wardana2,Patzek Tadeusz W.3

Affiliation:

1. King Abdullah University of Science and Technology, Sinopec Tech Middle East R&D Center, Saudi Arabia

2. University of Texas, Austin, USA

3. King Abdullah University of Science and Technology, Saudi Arabia

Abstract

Objective Reliable production forecasting for shale wells is crucial for investment decisions, optimized drilling rate, energy security policies, and informed green transition scenarios. The industry has struggled with inaccurate production estimates from decline curve analysis (DCA) and from a long production history requirement for data-driven models. We have developed a state-of-the-art, physics-guided, data-driven model for accurate production forecast of unconventional wells for up to 10 years into the future. With an error of less than 5%, our hybrid model requires only 1.5 years of production data. The method facilitates long-term production diagnostics, well survival probability estimates, and profitable economic decisions. Method The hybrid state-of-the-art production forecast method combines our τ-M physical scaling model with the higher-order derivatives of the production rate. For a set of 4000 wells, the first 1.5 years of production data were used to develop a universal hybrid model to estimate the pressure interference time, τ, for each well. The estimated τ is used to calculate the stimulated mass, M, of individual wells using the physical scaling curve. Finally, the data-driven estimate of τ, and physics-driven estimates of M are used to forecast future well production and well survival probability with time. Results The robustness of the hybrid model has been tested on 6000 new wells in the Barnett, Haynesville, Eagle Ford, and Marcellus shale plays. Using the initial 1.5 years of production data and a single hybrid model, the predicted pressure interference time, τ, for 6000 wells has an R2 of 0.98. The maximum error in the predicted cumulative production of 2000 Barnett wells for any given year between the 2nd year of production to the 15th year of production is only 2%. Similarly, the maximum error in the predicted cumulative production for Marcellus (500 wells), Haynesville, (1500 wells) and Eagle Ford (200 wells), is 2%, 5%, and 3%, respectively. The achieved outstanding accuracy is further used to calculate the well survival probability with time and optimize the future drilling rate required to sustain a given energy demand. Novelty We have developed a new, robust state-of-the-art hybrid model for unconventional well production forecasting. The model achieves an outstanding accuracy of > 95% and uses only the initial 1.5 years of production data. Early and accurate estimation of future production governs future investment decisions, re-fracking strategy, and improved energy security strategy.

Publisher

SPE

Reference25 articles.

1. Energy Information Administration (EIA), 2020. US Annual Natural Gas Production and Consumption, Today in Energy, 10July2020, https://www.eia.gov/todayinenergy/detail.php?id=44336#:~:text=Natural%20gas%20is%20one%20of,Tcf—both%20values%20were%20records

2. Energy Information Administration (EIA), 2020. Drilling Productivity Report, 15May 2023, https://www.eia.gov/petroleum/drilling/pdf/dpr-full.pdf

3. S&P Global Commodity Insights, Insight from Shanghai. Can shale gas secure China's energy security?28April2020, https://www.spglobal.com/commodityinsights/en/market-insights/blogs/natural-gas/042820-insight-from-shanghai-can-shale-gas-secure-chinas-energy-security

4. The US oil Majors set the pace in the Permian Basin;Crook,2022

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