Use of Drilling Parameters To Predict In-Situ Stress Bounds

Hareland, Geir; Hoberock, L.L.

doi:10.2523/25727-ms

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…A model of the drilling process for tricone bits called perfectcleaning model was derived by Warren and later modified by Hareland and Hoberok which is expressed as [5], [6]:…”

Section: Modified Warren Modelmentioning

confidence: 99%

Prediction of Rock Strength using Drilling Data and Sonic Logs

Amani¹,

Shahbazi²

2013

IJCA

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Uniaxial test (also called unconfined compression test) is one of most important tests used to measure rock strength. It is critical to obtain the rock strength parameters along the wellbore. Awareness of rock strength could better control drilling problems such as pipe sticking, tight hole, collapse, pack off and sand production. Rock strength also controls the drilling rate of penetration (ROP). The calculations of uniaxial Compressive Strength (UCS) are based on a simplified version of the ROP model proposed for tricone bits. The purpose of this research was to predict the uniaxial compressive strength based on sonic logs as a function of sonic travel and formation porosity. For obtaining continuous log strength along the wellbore in Ahwaz oilfield, quantitative relationships were developed between UCS and sonic travel time as well as UCS and both of sonic travel time and formation porosity. They actually are the outcome of regression analysis that resulting relationships are very practical and most often are specified by the researcher to the region of study. In this work, a larger well segment has been analyzed which there is no information break throughout the segment and it is investigated continuously. Using this approach, 3D model was a better predictor of uniaxial compressive strength than obtained equations related to transient time and results have also confirmed that the use of an recommended equations in this study can reduce drilling costs significantly.

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“…A model of the drilling process for tricone bits called perfectcleaning model was derived by Warren and later modified by Hareland and Hoberok which is expressed as [5], [6]:…”

Section: Modified Warren Modelmentioning

confidence: 99%

Prediction of Rock Strength using Drilling Data and Sonic Logs

Amani¹,

Shahbazi²

2013

IJCA

View full text Add to dashboard Cite

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“…In petroleum engineering, the knowledge of geomechanical properties and in situ stresses is essential to carry out drilling operations, analyze wellbore integrity, design secondary recovery, and estimate oil or gas production and fault reactivation risk 1–3 . Several techniques have been proposed to determine the in situ stresses, such as overcoring, jacking, strain recovery, borehole breakout, and hydraulic fracturing 4–6 . These methods have been briefly described, including their advantages and limitations, by Ljunggren et al 7 .…”

Section: Introductionmentioning

confidence: 99%

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Abreu

Mejia

Roehl

2022

Num Anal Meth Geomechanics

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In the last decades, the study of fluid flow through discontinuities such as fractures has received significant attention from the petroleum industry owing to the discovery of naturally fractured reservoirs. Predicting the behavior of this type of reservoir is challenging and includes, as a crucial step, determining its geomechanical parameters. Several in situ tests and empirical relationships have been established to estimate these parameters. Nevertheless, these procedures have important limitations. This work proposes an artificial intelligence-based methodology to identify geomechanical parameters from borehole injection pressure curves obtained during hydraulic fracturing tests. This methodology applies a genetic algorithm to minimize the sum of squared differences between the observed and predicted borehole pressure curves. Furthermore, an artificial neural network is adopted to build a proxy model that substitutes hydraulic fracturing numerical simulations, substantially reducing computational time. A proper recursive strategy is adopted to predict the borehole pressure curves.Additionally, a hyperparameter tuning technique selects appropriate neural network hyperparameters based on the multistep-ahead prediction error. Finally, the framework is applied in a KGD problem that models hydraulic fracture propagation. The benchmark confirms the capability of the proposed methodology to identify geomechanical parameters from fracturing tests.

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“…For estimating the in-situ stress magnitudes, vertical stress is obtained by integrating the formation density from top to bottom of a wellbore. The minimum horizontal stress magnitude is obtained directly from well tests such as leak-off test (LOT), extended leak-off test (XLOT), formation integrity test (FIT), minifrac test (Zoback, 2010;Fjar et al, 2008;Immerstein, 2013), and analysis of mud losses (Hareland and Hoberock, 1993). Performing the LOT/XLOT may weaken the formation and causes problems during drilling the rest of the well (Wang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Full waveform acoustic data as an aid in reducing uncertainty of mud window design in the absence of leak-off test

Aghajanpour

Fallahzadeh

Khatibi³

et al. 2017

Journal of Natural Gas Science and Engineering

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Creating a mechanical earth model (MEM) during planning the well and real-time revision has proven to be extremely valuable to reach the total depth of well safely with least instability problems. One of the major components of MEM is determining horizontal stresses with reasonable accuracy. Leak-off and minifrac tests are commonly used for calibrating horizontal stresses. However, these tests are not performed in many oil and gas wellbores since the execution of such tests is expensive, time-consuming and may adversely impact the integrity of the wellbore. In this study, we presented a methodology to accurately estimate the magnitudes and directions of horizontal stresses without using any leak-off test data. In this methodology, full waveform acoustic data is acquired after drilling and utilized in order to calibrate maximum horizontal stress. The presented methodology was applied to develop an MEM in a wellbore with no leakoff test data. Processing of full waveform acoustic data resulted in three far-field shear moduli. Then based on the acoustoelastic effect maximum horizontal stress was calibrated. Moreover, maximum horizontal stress direction was detected using this methodology through the whole wellbore path. The application of this methodology resulted in constraining the MEM and increasing the accuracy of the calculated horizontal stresses, accordingly a more reliable safe mud weight window was predicted. This demonstrates that the presented methodology is a reliable approach to analyze wellbore stability in the absence of leak-off test.

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Use of Drilling Parameters To Predict In-Situ Stress Bounds

Cited by 10 publications

References 10 publications

Prediction of Rock Strength using Drilling Data and Sonic Logs

Prediction of Rock Strength using Drilling Data and Sonic Logs

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Full waveform acoustic data as an aid in reducing uncertainty of mud window design in the absence of leak-off test

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