Analysis of stress field and determination of safe mud window in borehole drilling (case study: SW Iran)

Abdideh, Mohammad; Fathabadi, Mohammad Reza

doi:10.1007/s13202-013-0053-2

Cited by 29 publications

(16 citation statements)

References 6 publications

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“…Poisson's ratio and static Young's modulus are both calculated via the following relations in south west of Iran. The results show good conformity with laboratorial data (Abdideh and Fathabadi, 2013): (14) (15) Where the static Poisson's ratio and Es is the static Hayavi and Abedifar 53…”

Section: Calculation Of Rock Mechanical Propertiessupporting

confidence: 71%

A sensitivity study of relevant parameters on sand production in unconsolidated sandstone reservoirs

Hayavi¹,

Abedifar²

2017

J. Petroleum Gas Eng.

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Sand production is a prevalent problem during oil and gas production from weakly consolidated or unconsolidated formations. It can erode downhole equipment and surface facilities, cause pipeline blockage, leakage and damage casing and generate additional need for sand disposal. Decision for appropriate sand control strategy requires engineering analysis of the key parameters affecting sand production to evaluate timing and severity of sanding over the life of field conditions. This paper presents the effects of reservoir and geomechanical parameters including well trajectory, poroelastic stress coefficient, Biot's factore, maximum horizontal stress, horizontal stress anisotropy ratio, cohesive strength and Uniaxial Compressive Strength (UCS) on sand production from openhole wells. The results indicated that the critical bottomhole flowing pressure increases with increasing the poroelastic stress coefficient, Biot's factor, maximum horizontal stress and horizontal stress anisotropy ratio, but decreases with increasing the cohesive strength and UCS of rocks. Furthermore, the results show that the wellbore inclination and azimuth have a significant role in sanding potential during production. Also, it is concluded that in normal stress regime the critical bottom hole flowing pressure of a horizontal borehole is greater than the vertical borehole, so the vertical boreholes have less potential for sanding than the horizontal boreholes and almost all the deviated wells. Accurate prediction of the conditions for sand production is critical to the design of cost effective completion.

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Section: Calculation Of Rock Mechanical Propertiessupporting

confidence: 71%

A sensitivity study of relevant parameters on sand production in unconsolidated sandstone reservoirs

Hayavi¹,

Abedifar²

2017

J. Petroleum Gas Eng.

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“…In this study, the poroelastic model was used to determine the least principal stresses from (1) and (2) [9,16].…”

Section: Stresses Determinationmentioning

confidence: 99%

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Gowida

Ibrahim

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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The least principal stresses of downhole formations include minimum horizontal stress (σmin) and maximum horizontal stress (σmax). σmin and σmax are substantial parameters that significantly affect the design and optimization of the drilling process. These stresses can be estimated using theoretical equations in addition to some field tests, i.e., leak-off test to include the effect of tectonic stress. This approach is associated with many technical and financial issues. Therefore, the objective of this study is to provide a novel machine learning-based solution to estimate these stresses while drilling. First, new models were developed using artificial neural network (ANN) to directly predict σmin and σmax from the drilling data; which are injection rate (Q), standpipe pressure (SPP), weight on bit (WOB), torque (T), and rate of penetration (ROP). Such data are always available while drilling, and hence, no additional cost is required. Actual data from a Middle Eastern field were collected, statistically analyzed, and fed to the models. First, the models’ predictions showed a significant match with the actual stress values with a correlation coefficient (R-value) exceeding 0.90 and a mean absolute average error (MAPE) of 0.75% as a maximum. Second, new empirical equations were generated based on the developed ANN-based models. The new equations were then validated using another unseen dataset from the same field. The predictions had an R-value of 0.98 and 0.93 in addition to MAPE of 0.36% and 0.96% for σmin and σmax models, respectively. The results demonstrated the outperformance of the developed ANN-based equations to estimate the least principal stresses from the drilling data with high accuracy in a timely and economically effective way.

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“…So, they should be first converted into static data through some calculation changes made and then used in geomechanical model [Abdideh and Fathabadi, 2013]. …”

Section: Determining the Training And Test Setsmentioning

confidence: 99%

Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area

Hayavi¹,

Abdideh²

2016

Russ. J. Earth Sci.

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Accurate estimation of in situ stresses of a subsurface formation is important to get a basic knowledge of formation structure and position of anomalies, groundwater flows, performing fracturing operations, drilling operations, oil and/or gas production stimulation, wellbore stability analysis, and coupled geomechanics-reservoir simulation in petroleum engineering. In this paper, at first a new method for estimation of minimum and maximum horizontal stresses in tectonically active area based on the modification of linear poroelastic model and minifrac test results is presented. The rock mechanical properties used in poroelastic model are determinded using the artificial neural networks. Then, this method is applied to field data in order to verify the applicability of the modified linear poroelastic model. The results indicated that the agreement between the results of minifrac test and modified linear poroelastic model is satisfactory. Furthermore, application of artificial neural networks in this methodology increases the accuracy of linear poroelastic model for estimation of horizontal stresses. KEYWORDS: Minifrac tests; linear poroelastic model; horizontal stress; tectonically active area.Citation: Hayavi, Mohammad Tabaeh and Mohammad Abdideh (2016), Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area, Russ.

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Analysis of stress field and determination of safe mud window in borehole drilling (case study: SW Iran)

Cited by 29 publications

References 6 publications

A sensitivity study of relevant parameters on sand production in unconsolidated sandstone reservoirs

A sensitivity study of relevant parameters on sand production in unconsolidated sandstone reservoirs

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area

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