Estimation of minimum horizontal stress, geomechanical modeling and hybrid neural network based on conventional well logging data – a case study

Jamshidian, Majid; Zadeh, Mostafa Mansouri; Hadian, Mohsen; Nekoeian, Sahand; Zadeh, Morteza Mansouri

doi:10.1080/12269328.2016.1227728

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Assuming there is no change in the orthogonal normal stresses, the ratio of incremental normal stress to its resultant strain gives the Young's modulus for any mechanical body (Bjørlykke et al 2015). Dynamic Young's modulus can be obtained from elastic wave velocity from well logs (Archer and Rosouli 2012;Eyinla and Oladunji 2014;Jamshidian et al 2017) as given in Eq. 5:…”

Section: The Conceptual Frameworkmentioning

confidence: 99%

Application of pre-stack seismic waveform inversion and empirical relationships for the estimation of geomechanical properties in Ruby field, central swamp depobelt, Onshore Niger Delta, Nigeria

Ugbor

Odong

Akpan

2021

J Petrol Explor Prod Technol

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Pre-stack seismic inversion, well log analysis approach and empirical relations were adopted in this study to better estimate geomechanical properties of Ruby field with minimum error. The use of conventional well log empirical method alone to evaluate geomechanical properties in oil/gas fields sometimes becomes problematic. Geomechanical properties were divided into: elastic moduli [Young’s modulus, shear modulus, bulk modulus and Poisson ratio (PR)] and rock mechanical strength properties (closure stress ratio (CSR), brittleness (BRI) and compressibility). Four geomechanical earth models (CSR, BRI, Young’s modulus and PR) were generated from the inversion analysis to understand the distribution of rock strength properties across the field. The results deciphered high Young’s, shear and bulk modulus in the reservoir zone compared to the cap/seal rocks and a decrease in PR. This implies that, the cap/seal are more ductile and less compressible than the reservoir rocks, indicating that the reservoirs are highly brittle. CSR result reveals high in cap/seal indicating that the cap/seal rock are harder to fracture and has a greater chance to withstand higher compressive stress before failing as opposed to reservoir rocks. The inverted earth model shows that, Young’s modulus and brittleness increase toward the northeastern part of the field, while CSR and PR increase toward the southwestern part of the field. These results suggest that harder, stiffer, highly compressible and easily fractured rocks are found in the northern and eastern part of the field as opposed to the southern to western part of the field that is ductile.

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Section: The Conceptual Frameworkmentioning

confidence: 99%

Application of pre-stack seismic waveform inversion and empirical relationships for the estimation of geomechanical properties in Ruby field, central swamp depobelt, Onshore Niger Delta, Nigeria

Ugbor

Odong

Akpan

2021

J Petrol Explor Prod Technol

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“…The focus of this research is on young's modulus information and textural attributes beginning from sonic log by converting the compressional interval transit time to p-wave velocities and then applying well established empirical relations (Archer and Rasouli, 2012;Słota-Valim, 2013Jamshidian et al, 2017). The clear advantage of this method over measurement of static properties directly, using uniaxial loading test is that, it is less destructive to the specimen, more cost effective and time efficient (Zhang and Bentley, 2005).…”

Section: Introductionmentioning

confidence: 99%

The formation young’s modulus and textural attributes of the Axx-field from southern Niger delta, Nigeria

Joseph Gordian ATAT,

Emmanuel Bassey UMOREN,

Akaninyene Okon AKANKPO

et al. 2024

Int. J. Sci. Res. Updates

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This research was carried out to determine the formation brittleness and textural attributes of an Axx-Field in the Niger Delta Basin. Data available for this study were obtained from three wells (A11, A22 and A33). They were analyzed using Microsoft Excel after spurious values were removed. Two techniques (John Fuller and Plumb Bradford) were used to determine static young's modulus but only one is reported because after statistical analysis on both, the one reported was adequate for accurate outcomes although both approaches have low variance. The results indicates that the highest values of static and dynamic young's modulus are 2.05 x 1025N/m2 and 1.93 x 1010N/m2 respectively. The lowest values are 1.1 x 1024N/m2 and 8.5 x 109N/m2 respectively for well A11. The average values are 1.024 x 1025N/m2 and 5.25 x 1023N/m2 for this well. For well A22, the highest and lowest values of dynamic young’s modulus are 1.5848 x 1010N/m2 and 1.5726 x 1010N/m2 while those of static are correspondingly 1.47 x 1025N/m2 and 9.01 x 1014N/m2. Their average values are 1.5787 x 1010N/m2 and 7.35 x 1024N/m2 for dynamic and static young's moduli respectively. Also, for well A33, dynamic has the lowest value as 3.28 x 1010 N/m2; static has 8.36 x 1014N/m2 and their highest correspond to 2.04 x 1010N/m2 and 7.08 x 1025N/m2. The average value for this well are 2.66 x 1010N/m2 and 3.54 x 1025 N/m2 for dynamic and static respectively. The static Young's modulus results define the formation as brittle, whose environment is extremely poorly sorted, very fine skewed and very leptokurtic with low energy for well A11, very poorly sorted, fine skewed and platykurtic for well A22, extremely poorly sorted, very fine skewed and mesokurtic for well A33.

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“…Theoretically, Shmin can be determined by equation relating to other geomechanical parameters such as pore pressure and vertical stress [1]. On the other hand, empirical formulas were also established to determine minimum horizontal stress indirectly through sonic wave velocity measured from logging [2]. In reality, Shmin values determined from these empirical formulas still need to be calibrated with values obtained from the leak-off test (LOT).…”

Section: Introductionmentioning

confidence: 99%

Geomechanical Modelling Using Artificial Neural Networks Combined With Geostatistics

Phạm¹

2020

Preprint

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The principal minimum horizontal stress plays an important role in the study of reservoir characteristics, modeling of oil and gas reservoir, drilling, production and stimulating wells. However, it is currently not possible to measure the minimum horizontal stress along the wellbore as a geophysical parameter logging. Minimum horizontal stress is measured by leak-off test (LOT) at only several points in a well. In order to have the values all along the wellbore, experimental formulas were established to determine the minimum horizontal stresses for different fields. Then these formulas must be calibrated with LOT data whose number is usually limited, even sometimes unavailable. On the other hand, the empirical formulas of one field might not be accurate for another. This study presents a new approach to solve the problem of minimum horizontal stress estimation by a combination of artificial intelligence and geostatistics. The method consists of using artificial neural network (ANN) to build a model of minimum horizontal stress estimation from relevant parameters such as true vertical depth, pore pressure and vertical stress, then combined with Kriging interpolation to obtain the distribution in space of the minimum horizontal stress. Hence, this method can estimate the minimum horizontal stress with a limited amount of available data and therefore we do not need to drill new wells or to find empirical formulas for each survey area. The method was then applied in a case study involved real geomechanical dataset of Hai Thach - Moc Tinh field in Nam Con Son basin, Vietnam.

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Estimation of minimum horizontal stress, geomechanical modeling and hybrid neural network based on conventional well logging data – a case study

Cited by 11 publications

References 29 publications

Application of pre-stack seismic waveform inversion and empirical relationships for the estimation of geomechanical properties in Ruby field, central swamp depobelt, Onshore Niger Delta, Nigeria

Application of pre-stack seismic waveform inversion and empirical relationships for the estimation of geomechanical properties in Ruby field, central swamp depobelt, Onshore Niger Delta, Nigeria

The formation young’s modulus and textural attributes of the Axx-field from southern Niger delta, Nigeria

Geomechanical Modelling Using Artificial Neural Networks Combined With Geostatistics

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