Determination techniques of Archie’s parameters:a,mandnin heterogeneous reservoirs

Mohamad, A M; Hamada, G.M.

doi:10.1088/1742-2140/aa805c

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…In a carbonate reservoir as well as in a shaly sandstone reservoir, determination of Archie's parameters is tedious work due to electrical heterogeneity in the case of carbonate rocks, which can be attributed to the complicated grain pattern distribution, and in the case of shaly sandstone, is mainly due to the local conductivity created by clay minerals. The conventional determination of Archie's parameters can give acceptable values; however, the results are subjected to assumptions of full water saturation of the core samples in order to get the "a" and "m" parameters, and a homogeneous sample of the same porosity to get the saturation exponent "n" (Hamada 2017).…”

Section: Methodsmentioning

confidence: 99%

Petrophysical evaluation of sandstone gas reservoir using integrated well logs and core data for the Lower Cretaceous Kharita formation, Western Desert, Egypt

Osman

Kassab

Elgibaly

et al. 2021

J Petrol Explor Prod Technol

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This study aims to evaluate Kharita gas reservoir to enhance the production. The increase in water-cut ratio reduces the left hydrocarbons’ amount behind pipe. Accurate determination of pore throats, pores connectivity and fluid distribution are central elements in improved reservoir description. The integration of core and logging data responses is often used to draw inferences about lithology, depositional sequences, facies, and fluid content. These inferences are based on petrophysical models utilizing correlations among tools’ responses as well as rocks and fluids properties. Upper Kharita Formation produces gas and condensate from the clastic sandstone in Badr-3 field, western desert of Egypt. It consists mainly of sandstone with shale intercalations. It is subdivided into three sub-units Kharita A, Kharita B and Kharita C that are in pressure communication. Hence, a new further investigation and review for the previously calculated GIIP (gas initially in place) was initiated. The results of this study yielded that the main uncertainty in the volumetric calculations was the petrophysical evaluation; subsequently, a new unconventional petrophysical evaluation approach was performed. The sands thickness in Upper Kharita Formation varies between more than 9 up and more than 61 m with average porosity values range between 0.08 and 0.17 PU while the average permeability values range between 1.89 and 696.66 mD. The average hydrocarbon saturation values range between 46 and 97%. The sands thickness in Upper Kharita Formation varies between more than 9 up and more than 61 m with average porosity values range between 0.08 and 0.17 PU while the average permeability values range between 1.89 and 696.66 mD. The average hydrocarbon saturation values range between 46 and 97%. Reservoir shale cutoff of 55% by using cross-plot between shale volume and porosity (Toby Darling concept) was utilized to discriminate the reservoir from non-reservoir sections. The porosity model was used to calculate reservoir porosity, using the density log. The Archie and saturation/height function models were used to calculate the water saturation and used to calibrate the water saturation in the transition zone. The porosity–permeability (POR-PERM) transform equation was used to estimate the reservoir connectivity (absolute permeability) for the four petrophysical facies (High Quality Reservoir, Moderate Quality Reservoir, Low Quality Reservoir and Highly Shale Reservoir). Core data have shown variations in reservoir quality parameters (porosity and permeability) from one well to the other. Integration of all the reservoir pressures indicated that there are different fluid types (oil, gas and water) in the Upper Kharita Formation level. The saturation/height function model was used to calibrate the saturation in the transition zone. The integration of geological core and geophysical log data helped to conduct a comprehensive petrophysical assessment of Upper Kharita Formation for a better estimation of the reservoir and to achieve a better understanding of the water encroachment in the Upper Kharita reservoir. The big challenge is the determination of the most correct model for calculating porosity, permeability and water saturation in this reservoir of different quality sand. The new petrophysical evaluation resulted in doubling the volumes in Upper Kharita reservoir and so a perforation campaign was performed to confirm the new volumetric calculations, which showed a good match with the model results. Hence, a new well was drilled targeting the low quality sand and found them with high pressure almost near virgin pressure.

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Section: Methodsmentioning

confidence: 99%

Petrophysical evaluation of sandstone gas reservoir using integrated well logs and core data for the Lower Cretaceous Kharita formation, Western Desert, Egypt

Osman

Kassab

Elgibaly

et al. 2021

J Petrol Explor Prod Technol

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“…Learning process of PSONN was based on two steps: First step; PSO is designed for an optimal connected weights during neural networks, second step; it is the back propagation learning and training algorithm and applied to manipulate the nal weights. Figures (2) illustrates the PSONN technique learning performance that has reached 0.049926 at epoch of 417. In this case study, the neural network parameters (weights and basis) have been optimized by the PSO algorithm and the selected input parameters of neural network and system have been adopted as three layers.…”

Section: Arti Cial Intelligence Determination Of Archie's Parametersmentioning

confidence: 99%

“…Carbonate reservoirs have the characteristics of large reserves and high production, thus it is easy for them to form large oil and gas elds. However, in carbonate reservoirs, the dissolved vugs, pores, and fractures occupy a great proportion, resulting considerable petrophysical properties heterogeneity [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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“…vugs, oolites and fractures and very heterogeneous permeability distribution and pattern. This makes carbonate reservoirs seriously petrophysically heterogeneous reservoirs [1,2,3,4]. The use of experts' knowledge and statistics to the use of artificial intelligence (AI) models can provide good prediction of reservoir petrophysical properties from well-logs data.…”

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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Determination techniques of Archie’s parameters:a,mandnin heterogeneous reservoirs

Cited by 16 publications

References 14 publications

Petrophysical evaluation of sandstone gas reservoir using integrated well logs and core data for the Lower Cretaceous Kharita formation, Western Desert, Egypt

Petrophysical evaluation of sandstone gas reservoir using integrated well logs and core data for the Lower Cretaceous Kharita formation, Western Desert, Egypt

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

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

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