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This study introduces an advanced machine learning approach using an adaptive neuro-fuzzy inference system (ANFIS) to accurately predict static Young's modulus (Es) in hydrocarbon reservoirs. The ANFIS model demonstrates superior performance in determining rock types and improving petroleum engineering applications.

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Area of Science:

  • Petroleum Geosciences
  • Machine Learning Applications
  • Rock Mechanics

Background:

  • Static Young's modulus (Es) is vital for petroleum calculations.
  • Existing models for Es prediction lack precision and are limited in scope.
  • Accurate Es determination is crucial for geological assessments and reservoir engineering.

Purpose of the Study:

  • To develop and evaluate novel machine learning models for predicting static Young's modulus (Es).
  • To compare the performance of an adaptive neuro-fuzzy inference system (ANFIS) against existing models.
  • To assess the capability of ANFIS in identifying rock types based on predicted Es.

Main Methods:

  • Utilized adaptive neuro-fuzzy inference system (ANFIS) and other machine learning techniques.
  • Employed bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc) as predictor variables.
  • Trained and validated models on a diverse dataset of 1853 hydrocarbon reservoir rock samples.

Main Results:

  • The ANFIS model achieved an average absolute percentage relative error (AAPRE) of 5.1% and a correlation coefficient (R) of 0.9602.
  • ANFIS demonstrated superior predictive accuracy and faster decision-making compared to other machine learning methods.
  • Trend analysis confirmed that increasing RHOB increases Es, while increasing DTc and DTs decreases Es.

Conclusions:

  • ANFIS is the optimal model for predicting static Young's modulus (Es) with high accuracy.
  • The ANFIS model accurately captures the physical relationships between rock properties and Es.
  • This research offers significant advancements for geological assessments and petroleum engineering applications.