Open Access

ARTICLE

Modeling Techno-Economic Boundaries for Undeveloped Reservoirs: Integrated Simulation-Regression Approach with Xinjiang Case Study

Man Zhang¹, Cheng Chen¹, Hai-Xia Guo¹, Yi-Ming Xiao¹, Xin-Jian Zhao^2,*
1 Petroleum Exploration and Development Institute, Xinjiang Oilfield Company, Karamay, 834000, China
2 School of Economics and Management, Southwest Petroleum University, Chengdu, 610500, China
* Corresponding Author: Xin-Jian Zhao. Email:

Energy Engineering https://doi.org/10.32604/ee.2025.071943

Received 16 August 2025; Accepted 24 October 2025; Published online 20 November 2025

Abstract

Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated by Brent Crude’s trajectory from pandemic-induced negative pricing to geopolitically driven surges exceeding USD 100 per barrel. This study addresses these complexities through an integrated methodological framework applied to medium-permeability sandstone reservoirs in the Xinjiang oilfield by combining advanced numerical simulations with multivariate regression analysis. The methodology employs Latin Hypercube Sampling (LHS) to stratify geological parameter distributions and constructs heterogeneous reservoir models using Petrel software, rigorously validated through historical production data matching. Production forecasting integrates numerical simulation and Decline Curve Analysis (DCA), while investment estimation utilizes Ordinary Least Squares (OLS) regression to correlate engineering parameters with drilling and completion costs. Economic evaluation incorporates Discounted Cash Flow (DCF) modeling and breakeven analysis, establishing techno-economic boundaries via oil price sensitivity analysis ranging from USD 40 to 90 per barrel. Visualization tools, including 3D heatmaps, delineate nonlinear interactions among engineering, geological, and investment datasets under economic constraints. Key findings demonstrate that for the target reservoirs, as oil prices increase from USD 40 to USD 90 per barrel, the minimum economic thickness threshold decreases from approximately 5.7 m to about 2.5 m, with model prediction errors consistently below 25% across validation datasets. This framework provides scientifically grounded decision support for optimizing capital allocation and offers actionable insights to enhance undeveloped hydrocarbon development planning amid market uncertainty. Ultimately, it supports national energy security through technically robust and economically viable resource exploitation strategies.

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Keywords

Numerical simulation; multiple regression; technical-economic boundaries; EUR prediction; oil price sensitivity

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