Prediction of Porosity and Water Saturation Using Neural Networks in Shaly Sand Reservoirs, Western Deseret, Egypt

Hamada, G.M.; Sakka, Ahmed; Nyein, Chaw

doi:10.21608/jpme.2020.36116.1040

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…Table 3 shows a comparison of the statistical information of the proposed correlation with other empirical correlations. It can be seen from Table 3 that the proposed ANN empirical correlation gives AAPRE of 0.048 and MSE of 0.042, less than that obtained from Sayed et al (2022) 31 (AAPRE of 0.13 and MSE of 0.066) and Hamada et al 19 (AAPRE of 0.15 and MSE of 0.064). Statistically, the proposed ANN model is consistent and robust, based on the presented statistical analysis in Table 4 .…”

Section: Resultsmentioning

confidence: 56%

“…Groups of datasets from different fields in Egypt 19 were used in developing the ANN model. The data comprise nine inputs used for the training: the formation depth (DEPTH), the caliper size (CALI), the sonic time (DTR), gamma rays (GRs), shallow resistivity (LLS), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density (RHOB), and deep resistivity (Rt).…”

Section: Collected Data Analysismentioning

confidence: 99%

“…Artificial intelligence provides numerous benefits for petrophysical evaluation. Several researchers , have used ANN models, particularly feed-forward back-propagation neural networks (FFNNs), to develop more accurate predictions of the reservoir rock properties that include water saturation and porosity. An AI algorithm is developed for shaly sandstone reservoirs to predict water saturation, with the mean-square error (MSE) of 0.064.…”

Section: Application Of Ann To Predict Water Saturationmentioning

confidence: 99%

See 2 more Smart Citations

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

Azim¹,

Hamada²

2022

ACS Omega

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The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an R 2 of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient ( R 2 ) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation.

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Section: Resultsmentioning

confidence: 56%

Section: Collected Data Analysismentioning

confidence: 99%

Section: Application Of Ann To Predict Water Saturationmentioning

confidence: 99%

See 1 more Smart Citation

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

Azim¹,

Hamada²

2022

ACS Omega

Self Cite

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Section: Introductionmentioning

confidence: 99%

New Insights on Water Saturation Determination of Carbonate Reservoirs Using Artificial Intelligence Approach (AIA) and Conventional methods

hamada,

ElKadi,

Alkhudafi

2023

Journal of Petroleum and Mining Engineering

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Carbonate reservoir rocks are considered heterogeneous and it is due to complex pores pattern caused by different diagenetic factors that are modifying the microstructures and matrix system. parameters and finally leading to significant petrophysical heterogeneity and anisotropy. Water saturation determination in carbonate reservoirs is crucial parameter to determine initial reserve of given an oil field. Water saturation determination using electrical measurements is based on Archie's formula. Consequently, accuracy of Archie's formula parameters affects seriously water saturation values. This work focuses on calculation of water saturation using Archie's formula. Different determination techniques of Archie's parameters such as conventional technique, CAPE technique and 3-D technique have been tested and then water saturation was calculated using Archie's formula with the calculated parameters (a, m and n). This study introduced parallel self-organizing neural network (PSONN) algorithm predict Archie's parameters and determination of water saturation. Results have shown that predicted Archie's parameters (a, m and n) are highly accepted with statistical analysis lower statistical error and higher correlation coefficient than conventional determination techniques. The developed PSONN algorithm used big number of measurement points from core plugs of carbonate reservoir rocks. PSONN algorithm provided reliable water saturation values. We believe that PSONN can improve or may replace the conventional techniques to determine Archie's parameters and determination of reserve estimate in carbonate reservoirs.

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“…Wettability alteration is embarrassing factor in reservoir evaluation of clean and shaly reservoir rocks clay minerals aggravates the perception of heterogeneous reservoir complexity. [1,2,8,9,10,24,25] In this study, an arti cial intelligence technique, hybrid system (PSONN) was used to produce the required model to estimate Archie's parameters and thereby; predict water saturation curves in the studied well sections. Cementation exponent (m), tortuosity factor (a) and saturation exponent were calculated on the basis of Archie's equation in which water saturation values had already been predicted by the Hybrid Arti cial Intelligence model.…”

Section: Introductionmentioning

confidence: 99%

Parallel Self Organizing Neural Network (PSONN) Prediction of Water Saturation in Carbonate Reservoirs

Hamada

Al-Gathe

Al-Huzefei

2022

Preprint

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Carbonate reservoir rocks are considered heterogeneous and the distribution of reservoir quality in carbonates depends primarily upon how diagenetic processes are modifying the rock microstructure, leading to significant petrophysical heterogeneity and anisotropy. Water saturation determination in carbonate reservoirs is crucial parameter to determine initial reserve of given an oil field. Water saturation determination using electrical measurements is based on Archie’s formula. Consequently, accuracy of Archie’s formula parameters affects seriously water saturation values. Determination of Archie’s parameters (a, m and n) is proceeded by three techniques conventional, CAPE and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting an accepted value of Archie’s parameters and consequently reliable water saturation values. This work focuses on calculation of water saturation using Archie’s formula. Different determination techniques of Archie’s parameters such as conventional technique, CAPE technique and 3-D technique have been tested and then water saturation was calculated using Archie’s formula with the calculated parameters (a, m and n). This study introduced parallel self-organizing neural network (PSONN) algorithm predict Archie’s parameters and determination of water saturation. Results have shown that predicted Arche’s parameters (a, m and n) are highly accepted with statistical analysis lower statistical error and higher correlation coefficient than conventional determination techniques. The developed PSONN algorithm used big number of measurement points from core plugs of carbonate reservoir rocks. PSONN algorithm provided reliable water saturation values. We believe that PSONN can be supplement or even replacement of the conventional techniques to determine Archie’s parameters and then water saturation of carbonate reservoirs.

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Prediction of Porosity and Water Saturation Using Neural Networks in Shaly Sand Reservoirs, Western Deseret, Egypt

Cited by 4 publications

References 16 publications

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

New Insights on Water Saturation Determination of Carbonate Reservoirs Using Artificial Intelligence Approach (AIA) and Conventional methods

Parallel Self Organizing Neural Network (PSONN) Prediction of Water Saturation in Carbonate Reservoirs

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