Continuous Rock Strength Measurements On Core And Neural Network Modeling Result In Significant Improvements In Log-Based Rock Strength Predictions Used To Optimize Completion Design and Improve Prediction of Sanding Potential and Wellbore Stability (2003)

Continuous Rock Strength Measurements on Core and Neural Network Modeling Result in Significant Improvements in Log-Based Rock Strength Predictions Used to Optimize Completion Design and Improve Prediction of Sanding Potential and Wellbore Stability Roberto Suárez-Rivera; Roberto Suárez-Rivera TerraTek Search for other works by this author on: This Site Google Scholar Gary Ostroff; Gary Ostroff BHP Billiton Petroleum (Americas) Inc. Search for other works by this author on: This Site Google Scholar KaiSoon Tan; KaiSoon Tan BHP Billiton Petroleum (Americas) Inc. Search for other works by this author on: This Site Google Scholar Bill Begnaud; Bill Begnaud BHP Billiton Petroleum (Americas) Inc. Search for other works by this author on: This Site Google Scholar Wesley Martin; Wesley Martin TerraTek Search for other works by this author on: This Site Google Scholar Tony Bermudez Tony Bermudez TerraTek Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84558-MS https://doi.org/10.2118/84558-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Suárez-Rivera, Roberto, Ostroff, Gary, Tan, KaiSoon, Begnaud, Bill, Martin, Wesley, and Tony Bermudez. "Continuous Rock Strength Measurements on Core and Neural Network Modeling Result in Significant Improvements in Log-Based Rock Strength Predictions Used to Optimize Completion Design and Improve Prediction of Sanding Potential and Wellbore Stability." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84558-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search Abstract The scaling-up of laboratory rock mechanical measurements from sample-scale to reservoir-scale is fundamental to evaluation of wellbore stability, sanding potential, reservoir compaction or casing failure. Understanding rock heterogeneity is fundamental for adequate scaling-up laboratory measurements to core- and reservoir-scales and thus, to predictions of mechanical failure. Historically, scaling-up from core scale to reservoir scale has been dependent on calibration of log-based models to a sparsely sampled data set of rock mechanical property measurements made on core plugs. Such a sparsely sampled data set of core plug measurements alone may inadequately characterize the range heterogeneities in the reservoir, resulting in less than optimum log-based predictive models. With the introduction of continuous, high resolution, rock strength (UCS) measurements on core via scratch testing, an excellent calibration reference for producing robust log-based predictions of rock strength now exist.In this study, high-resolution measurements of strength heterogeneity were obtained as a function of core length and were correlated with fundamental textural and compositional parameters from petrographic analysis. Using adaptive learning neural networks, fundamental relationships between log measurements and rock strength were obtained. This methodology was adequate for characterizing the intrinsic rock heterogeneity at appropriate scales for mechanical analysis of completion design and sanding (0.25 ft). The methodology is also potentially applicable to the scaling-up of other fundamental mechanical properties such as in-situ strength, compressibility and thick-walled cylinder strength.Results show that intrinsic textural heterogeneity and strength heterogeneity are strongly related in sedimentary rocks. Recognizing the importance of rock heterogeneity and being able to scale-up this property to core and reservoir scales via log measurements results in significant improvements in the predictive capacity for sanding potential and wellbore stability. For example, thin layers of considerably weaker-strength than the surrounding rock, undetectable from conventional log-based rock strength predictions, were detected and included in the mechanical model. In addition to possessing high sanding potential, these weaker sections are also regions of fluid loss during drilling. Results can be used for selection of competent rock across the field (based on LWD measurements) for multilateral junction placement, and for selection of optimum completion strategies. Keywords: machine learning, mechanical property, strength heterogeneity, continuous uc measurement, log-based prediction, log analysis, log measurement, scratch measurement, prediction, reservoir geomechanics Subjects: Wellbore Design, Drilling Operations, Reservoir Characterization, Formation Evaluation & Management, Information Management and Systems, Exploration, development, structural geology, Reservoir geomechanics, Open hole/cased hole log analysis, Neural networks Copyright 2003, Society of Petroleum Engineers You can access this article if you purchase or spend a download.