Estimation of formation strength index of aquifer from neural networks

Hsieh, Bieng Zih; Wang, Chih‐Wen

doi:10.1016/j.cageo.2008.11.010

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The mechanical properties log (MPL) is applied in this study to distinguish between weak and strong sands by using the concept of dynamic elastic constants (Khamehchi and Reisi 2015;Osisanya 2010). According to Hsieh et al (2009), the major parameters that affect formation strength include bulk compressibility (C B ), shear modulus (G), uniaxial or unconfined compressive strength (UCS) and Young's modulus, (E). Compressional velocity, shear velocity and bulk density are obtained from sonic and density logs.…”

Section: Methodsmentioning

confidence: 99%

“…Major parameters that affect formation strength include bulk compressibility (C B ), shear modulus (G), unconfined compressive strength (UCS) and Young's modulus (Hsieh et al 2009). Tixier et al (1975) computed a critical value of 0.8 × 10 12 psi 2 for the MPL and 0.6 × 10 6 psi for the shear modulus with values greater than the set thresholds indicating no sanding problem.…”

Section: Mechanical Properties Log (Mpl)mentioning

confidence: 99%

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Modified approach for identifying weak zones for effective sand management

Sulaimon

Teng

2019

J Petrol Explor Prod Technol

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Sand production is a major problem that the oil and gas industry has been facing for years. It can lead to loss of production, equipment damage or complete well abandonment. Prediction of sand has been historically challenging due to the periodic nature of sand production, insufficient laboratory tests and lack of field tests validation. Analyses have been performed to identify weak zones for planned wells, and common technique is the application of shear modulus and mechanical properties log (MPL) criteria developed by Tixier et al. (J Pet Technol 27:283-293, 1975). However, the set criteria have been found to be generally inadequate to detect transition zone or predict weak formation in some fields. In this study, using the knowledge of rock behavior, geomechanical properties and well log data, we have established new simple criteria for identifying fragile sections within a transition zone. In situ logging data from a field X, located in Sabah, Malaysia, and Field Y, located in Shimokita, Japan, were used in this study. Using the threshold for shear modulus and MPL, the criteria for the geomechanical properties are set to differentiate formation strengths at different depths. The threshold for Poisson's ratio is 0.34, Young's modulus at 1.6 × 10 6 psi and the unconfined compressive strength at 2400 psi. The MPL and geomechanical models were generated to predict sanding incident. The results were subsequently validated with artificial neural network using MATLAB. Also, critical wellbore pressure is calculated and acts as a guide to operate outside the sand failure envelope. Thus, the prediction of the weak formation using geomechanical properties has been further established in this study.

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Section: Methodsmentioning

confidence: 99%

Section: Mechanical Properties Log (Mpl)mentioning

confidence: 99%