Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches – A study from the Badr El Din-1 field, Egypt

Farouk, Sherif; Sen, Souvik; Ganguli, Shib Sankar; Abuseda, Hesham; Debnath, Akash

doi:10.1016/j.marpetgeo.2021.105265

Cited by 28 publications

(9 citation statements)

References 49 publications

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“…Subsequently, these parameters were widely used in reservoir research 37 . The FZI and RQI values can be used to differentiate pore structure types with different seepage characteristics 38–42 . The derivation process is as follows:

{RQI}=0.0314\times {SQRT}(k/\varnothing ),

{\varnothing }_{z}={\varnothing }_{{He}}/(1-{\varnothing }_{{He}}),

\,{FZI}=\frac{{RQI}}{{\varnothing }_{z}}=\left(0.0314\times {\left(\frac{k}{\varnothing }\right)}^{\frac{1}{2}}\right)/({\varnothing }_{{He}}/(1-{\varnothing }_{{He}})),

where

k

is the permeability (mD),

{\varnothing }_{{He}}

is the effective porosity (%), and

{\varnothing }_{z}

is the ratio of pore volume to particle volume.…”

Section: Methodsmentioning

confidence: 99%

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Production prediction and main controlling factors in a highly heterogeneous sandstone reservoir: Analysis on the basis of machine learning

Wang

Tang

Cai

et al. 2022

Energy Science & Engineering

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Owing to a lengthy oil-bearing interval, strong anisotropism, and significant difference in the fluid properties of the sandstone oil reservoir in P Oilfield, it is quite challenging to accurately the productivity of the oil well at the initial stage. In this study, a deep neural network model is established, based on a gradient boosting algorithm, XGBoost, to forecast the initial productivity of oil wells, followed by an evaluation of the main controlling factors of productivity. One hundred oil wells in the study area were divided into training and verification groups. With a specific productivity index of an oil well with a stable period of approximately 6 months at the initial production stage as the target data, and geological, engineering, and oil reservoir parameters as input data, hyper-parameters for adjustment and optimization were selected, and a deep-learning-based unconsolidated sandstone productivity forecast model was established to forecast the initial productivity of oil wells in the target area. The mean square root error of the forecast result was <0.15, which is highly consistent with actual productivity. Finally, by adopting the XGBoost algorithm, the weight ranking of the controlling factors of productivity was clarified as follows: microscopic pore structure parameter > crude oil viscosity > median grain size > lithology index > well completion method > flow zone indicator. Machine learning has the advantages of effective forecasting of oil well productivity and the main controlling factors using multiple dimensions and big data.

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{RQI}=0.0314\times {SQRT}(k/\varnothing ),

{\varnothing }_{z}={\varnothing }_{{He}}/(1-{\varnothing }_{{He}}),

\,{FZI}=\frac{{RQI}}{{\varnothing }_{z}}=\left(0.0314\times {\left(\frac{k}{\varnothing }\right)}^{\frac{1}{2}}\right)/({\varnothing }_{{He}}/(1-{\varnothing }_{{He}})),

where

k

is the permeability (mD),

{\varnothing }_{{He}}

is the effective porosity (%), and

{\varnothing }_{z}

is the ratio of pore volume to particle volume.…”

Section: Methodsmentioning

confidence: 99%

“…37 The FZI and RQI values can be used to differentiate pore structure types with different seepage characteristics. [38][39][40][41][42] The derivation process is as follows: where k is the permeability (mD),  He is the effective porosity (%), and  z is the ratio of pore volume to particle volume.…”

Section: Reservoir Microscopic Characteristic Parametersmentioning

confidence: 99%

Production prediction and main controlling factors in a highly heterogeneous sandstone reservoir: Analysis on the basis of machine learning

Wang

Tang

Cai

et al. 2022

Energy Science & Engineering

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“…Sen et al integrated well logs, core measurements, and RF algorithms to address the petrophysical heterogeneity of the dolomite reservoir. Farouk et al deployed core data and two-hybrid ML algorithms, including PSO-trained neural networks and least-squares support vector machines (SVM), to evaluate the permeability of the reservoirs. Likewise, Al-Mudhafar employed a combination of machine learning and data analytics to enhance the geostatistical characterization of clastic reservoirs in the Luhais oil field.…”

Section: Introductionmentioning

confidence: 99%

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Manzoor

Ehsan

Hussain³

et al. 2023

Energy Fuels

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Petrophysical analysis is an industry-standard practice for reservoir evaluation as it provides critical inputs for characterizing subsurface formations and estimating resource potential. Khadro/Ranikot Formation sands are proliferous producers in the Central Indus Basin, Pakistan. The demarcate potential in intercalated sand shale layers that are thin and heterogeneous makes it a challenging reservoir. Conventional petrophysical interpretation is laborious and does not produce upto-mark results due to reservoir complexity, data limitations, and associated uncertainties. Hence, an emerging and delicate machinelearning (ML) approach has been comprehensively applied to analyze the potential and robustly interpret well log data while addressing the associated challenges. This case study entails a thorough evaluation of well log quality, assessing several algorithms such as least-squares support vector machines (one-class SVM), Random Forest Regressor (RFR), Extra Tree Regressor (ETR), Gradient Boosting Regressor (GBR), Decision Tree Classifier (DTC), etc. to compare their efficacy and reliability. One-class SVM helps to reduce outliers with great certainty, while the missing logs sonic (DT) and density (RHOB) are precisely predicted via GBR and ETR with 0.66 and 0.88 R 2 , respectively. Hence, providing reliable and optimized quality logs suitable for ML-based petrophysics. ML worked on these augmented logs by dividing the data into 60% training and 40% testing. The ETR outperformed the rest of the models with a correlation of 0.99 and 0.91 among conventional and ML results. Likewise, RFR performed exceptionally well for water saturation modeling, expressing the highest 0.93 correlation. Finally, DTC modeled reservoir facies with the best 91% accuracy and 0.935 F1 measures at the blind well. Excellent calibration of >85% is met with the estimates obtained by the predictive model compared to conventional methods. This comprehensive approach offers cost-effective and robust workarounds for modern formation evaluation with minimal uncertainty and resource-efficient multiwell interpretation within complex reservoirs and sets the stage for further research in the machine-learning ecosystem.

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“…When it comes to HMLMs, metaheuristic optimization algorithms have been used to determine hyperparameters of estimator algorithms in the process of training the models. Examples of such models include hybrid ANN-GA 1 and ANN-PSO 2 (Al Khalifah et al 2020;Farouk et al 2021;Kardani et al 2021;Matinkia et al 2022b;Tian et al 2022), PSO-XGBoost (Gu et al 2021;Liu 2022), PSO-SVM (Akande et al 2017;Mahdaviara et al 2020a;Yin et al 2020;Tian et al 2022), hybrid ELM-PSO and ELM-GA (Mahdaviara et al 2020a;Kardani et al 2021), and hybrid RF-PSO and RF-GA (Wang et al 2020;Tian et al 2022). Table 1 summarizes the studies focusing on the prediction of permeability by applying HML and SML models.…”

Section: Introductionmentioning

confidence: 99%

“…In their research, Mulashani et al (Mulashani et al 2022) used the gamma ray (GR), density (RHOB), effective porosity, shale volume, and neutron porosity (NPHI) logs for estimating the permeability. In another piece of work, P-wave travel time (DTCO), NPHI, formation resistivity (RT), GR, and RHOB logs were utilized to predict the permeability (Farouk et al 2021). Otchere et al (2021) based their work on the logs of caliper, DTCO, GR, NPHI, photoelectric (PEF), RHOB, resistivity log (RT) as well as rate of penetration (ROP) to model the permeability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Sheykhinasab

Mohseni

Bahari³

et al. 2022

J Petrol Explor Prod Technol

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Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R2 shed light on the MELM-COA as the best-performing model in the training and testing stages, as indicated by (RMSE = 0.5600 mD, R2 = 0.9931) and (RMSE = 0.6019 mD, R2 = 0.9919), respectively. The generalizability assessment conducted on the prediction of permeability in well B confirmed the MELM-COA can provide reliable permeability predictions by achieving an RMSE of 0.9219 mD. Consequently, the mentioned methodology is strongly recommended for predicting the permeability with high accuracy in similar depth intervals at other wells in the same field should the required dataset be available.

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Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches – A study from the Badr El Din-1 field, Egypt

Cited by 28 publications

References 49 publications

Production prediction and main controlling factors in a highly heterogeneous sandstone reservoir: Analysis on the basis of machine learning

Production prediction and main controlling factors in a highly heterogeneous sandstone reservoir: Analysis on the basis of machine learning

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

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