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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

A hybrid data-driven solution to facilitate safe mud window prediction.

Ahmed Gowida¹, Ahmed Farid Ibrahim¹, Salaheldin Elkatatny²

¹Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.

Scientific Reports

|September 21, 2022

Summary

This study introduces a data-driven artificial neural network model to estimate the safe mud weight (SMW) window for oil and gas wells. The new method accurately predicts safe mud weight limits, avoiding costly geomechanical analysis.

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Area of Science:

Petroleum Engineering
Geomechanics
Artificial Intelligence

Background:

Safe mud weight (SMW) window is crucial for drilling operations to prevent wellbore instability and fluid loss.
Conventional geomechanical analysis for determining SMW limits is often inaccessible or costly for many wells.
Accurate estimation of SMW is vital for efficient and safe oil and gas (O&G) well drilling.

Purpose of the Study:

To develop a novel data-driven model for estimating the safe mud weight window (SMW).
To create a cost-effective and time-efficient method for determining minimum mud weight for breakout (MWBO) and maximum mud weight for breakdown (MWBD).
To provide accurate estimations of MWBO and MWBD using readily available logging data.

Main Methods:

Development of artificial neural network (ANN) models to predict MWBO and MWBD directly from logging data.
Training and testing ANN models using actual field data from a Middle Eastern oil and gas field.
Derivation of simplified equations from optimized ANN models for direct calculation of MWBO and MWBD.

Main Results:

ANN models achieved high prediction accuracy, exceeding 92% for both MWBO and MWBD.
New equations derived from ANN models demonstrated high robustness and accuracy in validation tests.
The developed equations yielded a maximum mean absolute percentage error of only 0.60%.

Conclusions:

The data-driven ANN approach provides a timely and economically effective method for determining SMW limits.
The new equations facilitate accurate and efficient estimation of safe mud weight ranges using available logging data.
This approach offers a significant advantage over costly conventional geomechanical analysis for SMW determination.