Improved Data-Analysis Method Determines Archie Parameters From Core Data (includes associated paper 24964 )

Maute, R.E.; Lyle, W.D.; Sprunt, Eve

doi:10.2118/19399-pa

Cited by 39 publications

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“…(4) by plotting "Sw" vs "resistivity index". Maute et al (1991) introduced a mathematical technique to determine Archie's parameters m,n and optionally a from standard resistivity measurements on core samples. The idea of this technique is to determine the Archie's parameters by minimizing the mean square error between the measured and the computed saturations using the Archie' Equation.…”

Section: Conventional Determination Of a M And Nmentioning

confidence: 99%

Artificial Intelligence Based Estimation of Water Saturation Using Electrical Measurements Data in a Carbonate Reservoir

Bageri

Anifowose

Abdulraheem

2015

SPE Middle East Oil &Amp; Gas Show and Conference

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Good estimation of water saturation is necessary for successful estimation of reservoir properties as it minimizes the error in initial oil-in-place calculations. Electrical measurements on core plugs have been used to predict water saturation by analyzing the parameters of Archie's equation. Presently, several methods such as conventional, CAPE (1, m, n), CAPE (a, m, n) and 3D methods have been used to analyze the parameters. However, the accuracy of these methods has become inadequate for optimal estimations. Recent successful applications of Artificial Intelligence (AI) techniques in petroleum engineering have confirmed the capability of AI to handle such non-linear and complex industrial problems. Interestingly, the new paradigm of employing AI for water saturation estimation has not been sufficiently addressed in the petroleum engineering technology application literature.This study focuses on the use of the predictive capabilities of Artificial Neural Networks and the Fuzzy Inference Engine. 378 data samples obtained from the laboratory measurements of electrical properties of 41 core plugs taken from Carbonate reservoir rocks in the Middle East were used for the implementation of the proposed techniques. Using the popular stratified data sampling method, 70% of the data points were used to train the AI models while the remaining 30% were used for validation and testing.The comparative results showed that the AI models performed better with higher accuracy and lower errors than those obtained with the current methods. Based on this result, we conclude that Fuzzy Logic exhibits a robust predictive capability for the estimation of water saturation from electrical measurements by providing a good match with the core values and giving better error distribution than the other AI and current methods.

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Section: Conventional Determination Of a M And Nmentioning

confidence: 99%

Artificial Intelligence Based Estimation of Water Saturation Using Electrical Measurements Data in a Carbonate Reservoir

Bageri

Anifowose

Abdulraheem

2015

SPE Middle East Oil &Amp; Gas Show and Conference

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show abstract

“…Among the various methods available in the literature, Core Archie-Parameters Estimation (CAPE) (Maute et al 1992;Enikanselu and Olaitan 2013), which is an analysis method, is to estimate the Archie parameters a, m and n by minimizing the error between the computed water saturation and the measured water saturation. It is observed that the regressions of I R D S n w vs Sw and F vsˆbased on their plots are not the optimum way to obtain the Archie parameters.…”

Section: The Model Of Cape (Core Archie-parameters Estimation)mentioning

confidence: 99%

“…In (Maute et al 1992), a data analysis approach is proposed to determine the Archie parameters m; n and a based on standard resistivity measurements on the core samples. The simplex method is applied in (Chen et al 1995) to identify the three parameters of Archie equation.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

Liu

et al. 2015

Optimization Methods, Theory and Applications

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Archie formula, which contains three fundamental parameters (a, m, n), is the basic equation to compute the water saturation in a clean or shaly formation. These parameters are known as Archie parameters. To identify accurately the water saturation for a given reservoir condition, it depends critically on the accurate estimates of the values of Archie parameters (a, m, n). These parameters are interdependent and hence it is difficult to identify them accurately. So we present a new hybrid global optimization technique, where a gradient-based method with BFGS update is combined with an intelligent algorithm called Artificial Bee Colony. This new hybrid global optimization technique has both the fast convergence of gradient descent algorithm and the global convergence of swarm algorithm. It is used to identify Archie parameters in carbonate reservoirs. The results obtained are highly satisfactory. To further test the effectiveness of the new hybrid global optimization method, it is applied to ten non-convex benchmark problems. The outcomes are encouraging. Keywords Archie parameters • Hybrid global optimization • ABC algorithm • Gradient-based method IntroductionAn accurate identification of oil reserve in either an undeveloped or a developed reservoir is a significant task for a petro-physicist and reservoir engineer. To calculate the hydrocarbon reserve in its formation, it is required to know the amount of the water saturation. Inaccurate calculation of the amount of the water saturation will lead to a large error in the estimation of the hydrocarbon reserve.Archie equation, which is the underlying foundation for analyzing water saturation in potential oil and gas zones, is commonly used in the calculation of the amount of the water saturation of a reservoir rock, and hence providing an estimate of the initial hydrocarbon reserve of the reservoir. The values of the input parameters of Archie water saturation model in a clean or shaly formation must be estimated as accurately as possible. From field experiments, it is observed that the values of the following three input parameters, the cementation exponent m, the saturation exponent n, and the tortuosity factor a, depend critically on the petro-physical properties of a given rock. Thus, these parameters will take different values for different fields. Furthermore, the values of a, m and n in Archie formula are interdependent. Therefore, it is of critically importance that the estimations of the values of the parameters a, m and n are done accurately (Mabrouk et al. 2013;Michael et al. 2013;Makar and Kamel 2012).There are several techniques available in the literature for estimating the values of Archie parameters a; m and n. A conventional technique is to determine n separately, independent of a and m. This approach may not be valid in real situations, as it may lead to large error in the estimation of the amount of the water saturation (Archie 1942). In (Maute et al. 1992), a data analysis approach is proposed to determine the Archie parameters m; n an...

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“…Core Archie-Parameter Estimation (CAPE) Maute et al (1992) have presented a data analysis approach to determine Archie's parameters m and n and optionally n from standard resistivity measurements on core samples. The analysis method, Core Archie-Parameters Estimation (CAPE) determines m and n and optionally a by minimizing the error between computed water and measured water saturations.…”

Section: Conventional Determination Of a And Mmentioning

confidence: 99%

Analysis of Archie's Parameters Determination Techniques

Hamada

2010

Petroleum Science and Technology

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An accurate determination of oil reserve in either undeveloped or in developed reservoirs represents the main task of petrophysicist and reservoir engineer. Input parameters of Archie's water saturation model in clean formation or suitable shaly water saturation model must be determined as accurately as possible. Archie's parameters a, m, and n are the most important parameters to be estimated affecting determination of water saturation, in addition to the empirical constants required for selected empirical shaly water saturation model in case of shaly formations.This article presents a new technique to determine Archie's parameters called 3-D Technique. This technique is based on the three-dimension plot of three parameters; formation water resistivity, water saturation, and formation porosity. A comparison study with conventional technique and CAPE technique is conducted to outline the accuracy level of each technique defining Archie's parameters a, m, and n. Two field examples are given to show the applicability of three techniques and also to demonstrate the impact the uncertainty of Archie's parameters values on the accuracy of calculated water values using Archie's model or shaly water saturation model.

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Improved Data-Analysis Method Determines Archie Parameters From Core Data (includes associated paper 24964 )

Cited by 39 publications

References 0 publications

Artificial Intelligence Based Estimation of Water Saturation Using Electrical Measurements Data in a Carbonate Reservoir

Artificial Intelligence Based Estimation of Water Saturation Using Electrical Measurements Data in a Carbonate Reservoir

A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

Analysis of Archie's Parameters Determination Techniques

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