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Enhancing formation bulk density prediction while drilling using mud logging data and interpretable boosting machine learning

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Abstract

Mud-logging data, including drilling data (DD) and gas while drilling (GWD) parameters, are recorded in every oil and gas well drilled and are available for real-time assessment making them more beneficial for prompt assessment of formation assessment than the higher cost data provided by cores and wireline or measurement-while-drilling logs. The novel contribution of this research is that integrates DD and GWD data, to enhance prediction performance of formation bulk density in real time during drilling operations, It achieves this using interpretable machine learning Shapley Additive exPlanations (SHAP) analysis combined with four gradient-boosting models (CatBoost, Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost)) applied to data collected from two productive Algerian fields: Sif Fatima undersaturated (gas-poor) oil reservoir and Oued Zine saturated (gas-rich) oil reservoir. The results show that the DD and GWD inputs could effectively predict bulk density in both gas-poor and gas-rich oil reservoirs with various levels of precision. For gas-poor oil reservoirs, DD inputs, used in isolation generate predictions with low errors, particularly with model CatBoost4 (R≈0.905, RMSE≈0.040 g/cm3, MAE≈0.018 g/cm3, NSE≈0.815). For the gas-rich oil reservoir, DD inputs also achieved low errors (R≈0.945, RMSE≈0.025 g/cm3, MAE≈0.012 g/cm3, NSE≈0.845). However, the bulk formation density predictions of these reservoirs were better using GWD data in isolation (e.g., model CatBoost5: R≈0.991, RMSE≈0.009 g/cm3, MAE≈0.005 g/cm3, NSE≈0.980). These results are consistent with the expectations of gas-saturated oil reservoirs, as reported for real time porosity predictions. Good prediction performance was also achieved for the gas-rich oil reservoir by integrating both GWD and DD inputs, particularly with model AdaBoost:2 (R ≈ 0.985. RMSE ≈0.012 g/cm3, MAE≈0.009 g/cm3, NSE≈0.962). This investigation confirms that the use of Drilling Data (DD) is more effective for predicting bulk density in gas-poor reservoirs, while predictions using Gas While Drilling (GWD) data are more accurate in gas-rich reservoirs,This study also confirms the potential for real time bulk formation density predictions using mud logging parameters, justifying the compilation of larger datasets and comparing results from a wide range of reservoir formations and fluid types.

Highlights

• DD reliably predicts bulk density for gas-rich and gas-poor oil reservoirs.

• DD sensitivity analyses are distinctive for oil and gas-rich oil reservoirs.

• GWD provides the best bulk density prediction in gas-rich oil reservoirs.

• SHAP is an effective tool for interpreting input parameter prediction impacts.

• Mud logging data can reliably predict bulk density in different reservoirs.

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Data availability

No datasets were generated or analyzed during the current study.

Abbreviations

ANFIS:

Adaptive Neuro-Fuzzy Inference System

ANN:

Artificial Neural Network

C1:

Methane

C2:

Ethane

C3:

Propane

DD:

Drilling Data

EML:

Extreme Machine Learning

FN:

Functional Networks

GQR:

Gas Quality Ratio

GWD:

Gas While Drilling

iC4:

Isobutane

iC5:

Iso-pentane

LightGBM:

Light Gradient Boosting Machine

MAE:

Mean Absolute Error

ML:

Machine Learning

nC4:

N-Butane

nC5:

N-Pentane

NSE:

Nash-Sutcliffe Efficiency

RMSE:

Root Mean Square Error

R:

Correlation Coefficient

ROP:

Rate of Penetration

RF:

Random Forest

RPM:

Revolutions Per Minute

SHAP:

SHapley Additive exPlanations

SPP:

Standpipe Pressure

SVM:

Support Vector Machine

WOB:

Weight on Bit

XGBoost:

Extreme Gradient Boosting

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Acknowledgements

We gratefully acknowledge the help provided by Mr. Nordin Meddour and all members of SONATRACH, Berkine Groupement, Algeria, for their help in collecting data.

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Authors and Affiliations

  1. Laboratoire Des Réservoirs Souterrains: Pétroliers, Gaziers Et Aquifères, Université Kasdi Merbah Ouargla, 30000, Ouargla, Algeria

    Ayoub Boutaghane, Ouafi Ameur-Zaimeche, Rabah Kechiched & Nasreddine Tahar-Belkacem

  2. Faculty of Science, Agronomy Department, University 20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda, Algeria

    Salim Heddam

  3. Department of Earth and Geo-Environmental Sciences, University of Bari, Bari, Italy

    Abdelhamid Ouladmansour

  4. Basrah Oil Company, Basrah, Iraq

    Watheq J. Al-Mudhafar

  5. DWA Energy Limited, Lincoln, LN5 9JP, UK

    David A. Wood

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  1. Ayoub Boutaghane
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Contributions

Ayoub Bouataghane: Data Curation, Conceptualization, Software, Writing-original draft preparation, Writing Review and Editing. Ouafi Ameur-Zaimeche: Supervision, Methodology, Software, Interpretation of the Results, Writing-original draft preparation, Writing Review and Editing. Salim Heddam: Supervision, Software, Investigation, Visualization, Writing Review and Editing. Rabah Kechiched: Interpretation of the Results, Writing-original draft preparation, Writing Review and Editing. Nasreddine Tahar-Belkacem: Software, Investigation, Visualization, Writing Review and Editing. Abdelhamid Ouladmansour: Software, Investigation, Visualization, Writing Review and Editing. Watheq J. Al-Mudhafar: Interpretation of the Results, Validation, Writing Review and Editing. David A. Wood: Interpretation of the Results, Validation, Writing Review and Editing.

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Correspondence to Ouafi Ameur-Zaimeche.

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Communicated by: Hassan Babaie

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Boutaghane, A., Ameur-Zaimeche, O., Heddam, S. et al. Enhancing formation bulk density prediction while drilling using mud logging data and interpretable boosting machine learning. Earth Sci Inform 18, 172 (2025). https://doi.org/10.1007/s12145-024-01642-7

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