Machine learning models for generating the drilled porosity log for composite formations

Gamal, Hany; Elkatatny, Salaheldin; Mahmoud, Ahmed Abdulhamid

doi:10.1007/s12517-021-08807-4

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…The conventional practice used for the assessment of porosity is lab-based measurement of rock samples as well as traditional logging methods, which encounter constraints related to cost, and time. To address this limitation, a machine learning-based method has been employed for estimating porosity based on drilling data [47,54]. Figure 10 shows the cross plot between the potassium and thorium, representing the amount of minerals present in the reservoir formation.…”

Section: Mud-bearing Asphaltene Fine-grained Feldspathic Quartz Sands...mentioning

confidence: 99%

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

Feng,

Wang,

et al. 2024

JMSE

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A growing number of large data sets have created challenges for the oil and gas industry in predicting reservoir parameters and assessing well productivity through efficient and cost-effective techniques. The design of drilling plans for a high-pressure tight-sand reservoir requires accurate estimations of pore pressure (Pp) and reservoir parameters. The objective of this study is to predict and compare the Pp of Huizhou Sag, Pearl River Mouth Basin, China, using conventional techniques and machine learning (ML) algorithms. We investigated the characteristics of low-permeability reservoirs by observing well-logging data sets and cores and examining thin sections under a microscope. In the reservoir zone, the average hydrocarbon saturation is 55%, and the average effective porosity is 11%. The tight sandstone reservoirs consist of fine- to extremely fine-grained argillaceous feldspathic sandstone. The mean absolute error for reservoir property prediction is 1.3%, 2.2%, and 4.8%, respectively, for effective porosity, shale volume, and water saturation. Moreover, the ML algorithm was employed to cross-check the validity of the prediction of Pp. Combining conventional and ML techniques with the core data demonstrates a correlation coefficient (R2) of 0.9587, indicating that ML techniques are the most effective in testing well data. This study shows that ML can effectively predict Pp at subsequent depths in adjacent geologically similar locations. Compared to conventional methods, a substantial data set and ML algorithms improve the precision of Pp predictions.

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Section: Mud-bearing Asphaltene Fine-grained Feldspathic Quartz Sands...mentioning

confidence: 99%

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

Feng,

Wang,

et al. 2024

JMSE

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“…The subtractive clustering is used to group the data based on its potential density to identify the cluster center compared to the surrounding data points. Recently, ANFIS-SC showed high accuracy in solving petroleum engineering-related problems (Mahmoud et al, 2021b;Gamal et al, 2021). The third model is support vector regression (SVR) which works based on the principles of the support vector machine (SVM) developed by Vapnik, 1998.…”

Section: Concepts Of Machine Learning Modelsmentioning

confidence: 99%

Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms

et al. 2022

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The dynamic Young’s modulus (Edyn) is a parameter needed for optimizing different aspects related to oil well designing. Currently, Edyn is determined from the knowledge of the formation bulk density, in addition to the shear and compressional velocities, which are not always available. This study introduces three machine learning (ML) models, namely, random forest (RF), adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), and support vector regression (SVR), for estimation of the Edyn from only the real-time available drilling parameters. The ML models were learned on 2054 datasets collected from Well-A and then tested and validated on 871 and 2912 datasets from Well-B and Well-C, respectively. The results showed that the three optimized ML models accurately predicted the Edyn in the three oil wells considered in this study. The optimized SVR model outperformed both the RF and ANFIS-SC models in evaluating the Edyn in all three wells. For the validation data, the Edyn was assessed accurately with low average absolute percentage errors of 3.64%, 6.74%, and 1.03% using the optimized RF, ANFIS-SC, and SVR models, respectively.

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“…Machine learning (ML) algorithms offer promising solutions for different problems by analyzing vast amounts of data and identifying complex patterns and relationships that may not be apparent to human analysts. − By training the models on a data set that includes both input parameters and corresponding output measurements, the algorithms can learn the underlying patterns and create predictive models capable of estimating formation properties. Different authors discussed the application of machine learning in predicting formation porosity and permeability. − …”

Section: Introductionmentioning

confidence: 99%

“…Different authors discussed the application of machine learning in predicting formation porosity and permeability. 22 − 29 …”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters

Hassaan,

Mohamed,

Ibrahim

et al. 2024

ACS Omega

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The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (k) obtained through core analysis. Three machine learning models, namely, decision trees (DTs), random forest (RFs), and support vector machines (SVMs), were employed and evaluated for their effectiveness in predicting porosity and permeability. The results demonstrate promising performance across the different data sets. All three models achieved correlation coefficients (R) higher than 0.91 in predicting porosity. The RF model exhibited accurate predictions of permeability, achieving R values surpassing 0.92 in the various data sets. While the DT model displayed slightly lower performance, with the R-value decreasing to 0.88 in the testing data set, the SVM model suffered from overfitting, with R values dropping to 0.83 in the testing data set. The novelty of this work lies in the successful application of machine learning models to the real-time prediction of reservoir properties, providing a practical and efficient solution for the oil and gas industry. By achieving correlation coefficients exceeding 0.91 and showcasing the models' efficacy in a dynamic testing data set, this study paves the way for improved decision-making processes and enhanced exploration and production activities. The innovative aspect lies in the utilization of drilling parameters for timely and cost-effective estimation, transforming conventional reservoir evaluation methods.

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Machine learning models for generating the drilled porosity log for composite formations

Cited by 7 publications

References 39 publications

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms

Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters

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