Determination of minimum and maximum stress profiles using wellbore failure evidences: a case study—a deep oil well in the southwest of Iran

Molaghab, Abdollah; Taherynia, Mohammad Hossein; Aghda, Seyed Mahmoud Fatemi; Fahimifar, Ahmad

doi:10.1007/s13202-017-0323-5

Cited by 27 publications

(5 citation statements)

References 26 publications

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“…The stress magnitude can be estimated either by employing field tests or using developed theoretical-based equations. The equations developed based on the poroelastic model are considered the most common and applicable method to estimate the stress profile at the desired depth of the drilled wells. ,, Blanton and Oslon were the first to introduce anisotropy in the in situ horizontal stress equation for different lithologies. Their model considers the effect of the tectonic stresses by introducing the tectonic strains into the equation.…”

Section: Discussionmentioning

confidence: 99%

“…These solutions include determining the optimum mud weight, defining the safe drilling window, specifying stable trajectories, determining casing setting depths, and so forth . Furthermore, defining the downhole stress condition or distribution is considered the cornerstone for developing a representative geomechanical model of subsurface formations whereby a broad suite of problems along different stages of the reservoir life could be addressed and resolved. − …”

Section: Introductionmentioning

confidence: 99%

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New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data

et al. 2022

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The maximum (Sh max ) and minimum (Sh min ) horizontal stresses are essential parameters for the well planning and hydraulic fracturing design. These stresses can be accurately measured using field tests such as the leak-off test, step-rate test, and so forth, or approximated using physics-based equations. These equations require measuring some in situ geomechanical parameters such as the static Poisson ratio and static elastic modulus via experimental tests on retrieved core samples. However, such measurements are not usually accessible for all drilled wells. In addition, the recently proposed machine learning (ML) models are based on expensive and destructive tests. Therefore, this study aims at developing a new approach to predict the least principal stresses in a time- and cost-effective way. New models have been developed using ML approaches, that is, artificial neural network (ANN) and support vector machine (SVM), to predict Sh min and Sh max gradients (outputs) from well-log data (inputs). A wide-ranged set of actual field data were collected and extensively analyzed before being fed to the algorithms to train the models. The developed ANN-based models outperformed the SVM-based ones with a mean absolute average error (MAPE) not exceeding 0.30% between the actual and predicted output values. Besides, new equations have been developed to mimic the processing of the optimized networks. The new empirical equations were verified by another unseen data set, resulting in a remarkably matched actual stress-gradient values, confirmed by a prediction accuracy exceeding 90% in addition to an MAPE of 0.43%. The results’ statistics confirmed the robustness of the developed equations to predict the Sh min and Sh max gradients with a high degree of accuracy whenever the logging data are available.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data

et al. 2022

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“…The vertical stress or overburden stress can be resolved by the integration of wireline geophysical density logs (Equation 1) (Bell 1996;Musolino et al n.d.-b) or by using a correlation with sonic velocity (Gardner et al 1974;Ludwig et al 1970). SHmax can then be constrained by a number of observations such as the occurrence of breakouts, the measurement of breakout widths or by the occurrence of DITF (Barton et al 1988;Hubbert & Willis 1957;Kirsch 1898;Molaghab et al 2017;Moos & Zoback 1990;Zoback 2010).…”

Section: Loading Methodsmentioning

confidence: 99%

“…Acoustic emissions testing in our dataset produced stress magnitudes similar to that of overcore however, the mean orientations rotated 83°, 309°N for overcore and 32°N for AE, requiring other methods to determine reliability. 3, where Co = compressive rock strength (MPa) (Molaghab et al 2017;Moos & Zoback 1990). If acoustic televiewer logs are available, specialist geophysical log software can allow for the measurement of borehole breakouts (Figure 2).…”

Section: Relief Methodsmentioning

confidence: 99%

In situ stress data management and analysis: implications for numerical modelling

Musolino¹,

Chester²,

Ford³

2022

Caving 2022: Fifth International Conference on Block and Sublevel Caving

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The measurement of in situ stresses is a costly and time intensive procedure, requiring significant experience, diligence and planning to obtain reliable results. Therefore, it is important to have good data management and analysis to maintain investment costs and reduce estimate uncertainty. Geotechnical design considerations rely on accurate estimates of the stress regime to predict subsurface rock behaviour. Some uses include cave deconfinement, cave preconditioning, planning drive orientation and ground support design. Two simple numerical models are presented. The first demonstrates stress redistribution around a cave zone and how it may influence optimal crusher chamber placement. The second model is of an undercut level and the stress experienced by the drawbell drives below. The models demonstrate the impact of in situ stress uncertainty, with potential consequences for design decisions by altering the stress trend and plunge. Currently, pragmatic workflows are not publicly available that holistically detail the key steps in in situ stress data analysis. In addition, rarely are end-users of the data demonstrated the uncertainty in the values provided. Instead, they are presented with a single stress average gradient and trend. This manuscript offers a workflow for those embarking on in situ stress testing campaigns and serves as a tool to communicate the complexities to potential end-users. The workflow utilises the Euclidian mean rather than scalar averaging of the stress tensor and relates 'loading methods' (hydrofracking) and 'other' (borehole breakout observations) methods of stress estimation with the full tensor resolved from relief methods (overcore and acoustic emissions). A software application within the mXrap suite is presented as a potential aid to stress data management and analysis. Implications of stress uncertainty are demonstrated and discussed concerning design, expected induced fracture orientations, and future workflow enhancements. The numerical models indicate optimal placement for a crusher chamber around a simulated cave zone was observed to be in the orientation of SHmax. Increasing the stress field plunge by 20° impacted optimal crusher placement. The second numerical model demonstrates that a rotation of the horizontal stress trend by 20° has noticeable impact on the differential stress in the backs of a tunnel where drawbells will be created, this has implications for planning the undercut veranda length.

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“…On the other side, in nonshale intervals of permeable formations with intragranular connectivity, pressure points of the hydrostatic column can be set. Principal in-situ stresses are the crucial parameters in geomechanics modeling where different methods can be used for calculation (Molaghab et al 2017). In the plane strain concept it is assumed that ε z = 0 but σ x , σ y , σ z ≠ 0 along the wellbore axis.…”

Section: Rock Mechanical Propertiesmentioning

confidence: 99%

Development of a Geomechanics Program for Wellbore Stability Analysis

Saeidi

Dalkhani

2023

Int. J. Geomech.

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A geomechanics program for wellbore stability analysis has been developed consisting of two modules: an analytical-based solution and a numerical-based solution. In the first part, input data are imported, including petrophysical well logs, pressure data, formation well tops, and a well path. Lithology intervals are set with proper prediction equations to calculate rock mechanical properties based on laboratory tests. In-situ stress and pore pressure are determined using different methods, including the poroelastic plane strain model and stress polygon. From the theory of plane strain, new equations are solved to determine horizontal tectonic strains (ε h , ε H ) from drilling events such as total mud loss and breakout during drilling. Safe mud weight bounds are calculated through depth and in different azimuths and inclinations applying the Mohr-Coulomb and the Mogi-Coulomb failure criteria. The latter underestimated the minimum mud weight to prevent wellbore breakout. The transversely vertical isotropy of shale formation is programmed with multiple stress transformations via the weak-plane method. In the second module, a 3D model around the wellbore is discretized with hexahedral eight-point elements and programmed using the finite-element (FE) method. Rock mechanical property and displacement boundary conditions are applied to solve FE equations. Stress from the numerical model matched to the Kirsch model and results show that maximum stress concentration around the wellbore corresponds to the wellbore breakout, which has analytically been established. A new well plan across the 3D model was examined to obtain the safe mud weight bounds and results were in agreement with the analytical calculations.

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Determination of minimum and maximum stress profiles using wellbore failure evidences: a case study—a deep oil well in the southwest of Iran

Cited by 27 publications

References 26 publications

New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data

New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data

In situ stress data management and analysis: implications for numerical modelling

Development of a Geomechanics Program for Wellbore Stability Analysis

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