A Composite Determination of Mechanical Rock Properties for Stimulation
Design (What to Do When You Don’t Have a Sonic Log)

Mullen, Michael; Roundtree, Russell; Turk, George F.

doi:10.2523/108139-ms

Cited by 26 publications

(18 citation statements)

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“…For practical purposes, in rocks mainly composed of shale like in this study, there is no difference between the dynamic and static Poisson's ratio (Mullen et al, 2007). The static Young's modulus E s is obtained from the dynamic moduli E D measured in the well logs using (Mullen et al, 2007)…”

Section: Study Areamentioning

confidence: 99%

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Modeling of the in situ state of stress in elastic layered rock subject to stress and strain-driven tectonic forces

Roche

Baan

2017

Solid Earth

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Abstract. In this study we describe and compare eight different strategies to predict the depth variation of stress within a layered rock formation. This reveals the inherent uncertainties in stress prediction from elastic properties and stress measurements, as well as the geologic implications of the different models. The predictive strategies are based on well log data and in some cases on in situ stress measurements, combined with the weight of the overburden rock, the pore pressure, the depth variation in rock properties, and tectonic effects. We contrast and compare stresses predicted purely using theoretical models with those constrained by in situ measurements. We also explore the role of the applied boundary conditions that mimic two fundamental models of tectonic effects, namely the stress-or strain-driven models. In both models, layer-to-layer tectonic stress variations are added to initial predictions due to vertical variation in rock elasticity, consistent with natural observations, yet describe very different controlling mechanisms. Layer-to-layer stress variations are caused by either local elastic strain accommodation for the strain-driven model, or stress transfers for the stressdriven model. As a consequence, stress predictions can depend strongly on the implemented prediction philosophy and the underlying implicit and explicit assumptions, even for media with identical elastic parameters and stress measurements. This implies that stress predictions have large uncertainties, even if local measurements and boundary conditions are honored.

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Section: Study Areamentioning

confidence: 99%

“…The static Young's modulus E s is obtained from the dynamic moduli E D measured in the well logs using (Mullen et al, 2007)…”

Section: Study Areamentioning

confidence: 99%

Modeling of the in situ state of stress in elastic layered rock subject to stress and strain-driven tectonic forces

Roche

Baan

2017

Solid Earth

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“…Dynamic sonic data is an essential element in defining rock mechanical properties for hydraulic fracture modelling in clastics as well as coals (Mullen et al, 2007). Standard triaxial testing is often used for establishing static rock mechanical property correlations for clastics (Morales and Marcinew, 1993).…”

Section: Geophysical Logsmentioning

confidence: 99%

“…To obtain the necessary estimates for the rock mechanical properties to complete a model, the research used internal rock mechanical property correlations in GOHFER ® , based on the gamma ray log and derived lithology models. GOHFER ® has several models for estimating rock mechanical properties for various rocks, the most simplistic being the gamma ray, lithology based, correlation model; whilst simplistic this model has been proven effective in clastics (Johnson and Rodgerson, 1998;Mullen et al, 2007). However, without any data from laboratory testing or DFIT, these rock mechanical property correlations and their resultant  h-min profiles can be misleading; as noted earlier, the author is concerned that a representative sample of material for mechanical properties testing can be obtained from 500 m for a composite, relatively compressible material like coal without maintaining bottomhole pressure conditions on the coal throughout the process.…”

Section: Rock Properties Data and Inputmentioning

confidence: 99%

“…GOHFER ® can take geophysical log data and process dynamic sonic data to estimate static material properties and reservoir parameters. Even without full wave form sonic data, rock mechanical parameters can be estimated within the petrophysical model of GOHFER ® using correlations, inferences from other log measurements, or from surrounding wells (Mullen et al, 2007).…”

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Modifying subterranean fluid flow patterns using barriers to improve pre-drainage in coal mining

Johnson¹,

Raymond²

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Gas drainage has been performed in advance of coal mining for commercial production of gas and to reduce levels of gas to safe or regulatory levels required for coal mining. Inadequate pre-drainage can lead to higher levels of gas than may be adequately managed by ventilation systems, potential pressure scenarios, or even outbursts, any of which can affect mine safety and economics. Thus, it may be desirable to more rapidly depressurise the coal seam based on mine safety and production scheduling, currently requiring either stimulation of the coal or additional drilling of pre-drainage wells and potentially jeopardising project economics.

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The role of lithological layering and pore pressure on fluid‐induced microseismicity

Roche

Baan

2015

JGR Solid Earth

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The success of hydraulic fracturing treatments is often judged by the shape and size of the resulting microseismic cloud. However, it is challenging to predict the anticipated microseismic cloud prior to treatment. We use geomechanical modeling to predict the distribution of the microseismicity prior to the hydraulic fracture treatment. We analyze the likelihood of tensile and shear failure due to 1-D variations in local stresses and rock strengths, induced by layering and pore pressure, for two field cases. The deviation in the local stresses from the regional stress field is induced by vertical variations in stiffness. This promotes failure of the stronger layers instead of the weaker ones since the stronger layers can become essentially load bearing. The simulations and field studies show that (1) microseismic events tend to locate preferentially where layers reach tensile failure due to fluid injection, and the number of events tends to decrease in layers that do not reach tensile failure; (2) shear initiation can occur in different layers from those failing in tension, thereby creating additional fluid migration paths; and (3) reactivation of preexisting fractures may occur due to fluid migration, even if their orientations are unfavorable. Numerical modeling is a significant aid in understanding the interplay of regional and local stresses and associated in situ failure due to variations in rock strength, pore pressure, and stiffnesses. In a wider perspective, it gives fundamental insights into the understanding of earthquakes and fault localization, the mechanisms of fracture development, and the role of fractures on fluid circulation and on the in situ stress field.

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A Composite Determination of Mechanical Rock Properties for Stimulation Design (What to Do When You Don’t Have a Sonic Log)

Cited by 26 publications

References 0 publications

Modeling of the in situ state of stress in elastic layered rock subject to stress and strain-driven tectonic forces

Modeling of the in situ state of stress in elastic layered rock subject to stress and strain-driven tectonic forces

Modifying subterranean fluid flow patterns using barriers to improve pre-drainage in coal mining

The role of lithological layering and pore pressure on fluid‐induced microseismicity

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