Special Oil & Gas Reservoirs >

2026 , Vol. 33 >Issue 3: 62 - 71

DOI: https://doi.org/10.3969/j.issn.1006-6535.2026.03.007

Reservoir Engineering

A production prediction method for shale oil in mud shale formations based on RIME-XGBoost

  • YANG Erlong ,
  • YIN Shitan ,
  • WANG Yu ,
  • WANG Xianjun ,
  • DONG Chi
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  • 1. Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education,Northeast Petroleum University,Daqing,Heilongjiang 163318,China;
    2. Oil Production Engineering Research Institute,PetroChina Daqing Oilfield Co., Ltd.,Daqing,Heilongjiang 163453,China

Received date: 2025-07-15

  Revised date: 2026-02-03

  Online published: 2026-09-04

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Abstract

Accurate production prediction remains difficult for hydraulically fractured horizontal wells in mud shale oil reservoirs because numerous geological and engineering factors jointly affect well performance.To solve this problem,a big-data preprocessing method for shale oil production prediction was proposed by combining retrieval-augmented generation(RAG) technology with the DeepSeek large language model,increasing data collection efficiency by six times.A RIME-XGBoost model suitable for production prediction in mud shale reservoirs was constructed to achieve intelligent prediction of cumulative oil production from hydraulically fractured horizontal wells,and SHAP-based interpretability analysis was used to identify the key factors controlling production.The results show that the main controlling factors of mud shale oil production are vitrinite reflectance (Ro),compensated neutron,proppant intensity,sand-fluid ratio,logging-derived effective porosity,maximum horizontal principal stress,and total hydrocarbon from gas logging.The RIME-XGBoost model yields a coefficient of determination(R2) of 0.936 and a prediction accuracy of 91.04%,demonstrating both high accuracy and strong interpretability.This study effectively addresses problems such as low efficiency in data preprocessing and poor model interpretability in traditional production prediction,and provides a data-driven solution for accurate productivity prediction and fracturing parameter optimization in mud shale oil development,which is of great significance for the efficient development of mud shale oil.

Key words： mud shale oil; machine learning; production prediction; intelligent fracturing; geology-engineering integration

Cite this article

YANG Erlong , YIN Shitan , WANG Yu , WANG Xianjun , DONG Chi . A production prediction method for shale oil in mud shale formations based on RIME-XGBoost[J]. Special Oil & Gas Reservoirs, 2026 , 33(3) : 62 -71 . DOI: 10.3969/j.issn.1006-6535.2026.03.007

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