Dataset Information

An improved geomechanical model for the prediction of fracture generation and distribution in brittle reservoirs.

ABSTRACT: It is generally difficult to predict fractures of low-permeability reservoirs under high confining pressures by data statistical method and simplified strain energy density method. In order to establish a series of geomechanical models for the prediction of multi-scale fractures in brittle tight sandstones, firstly, through a series of rock mechanics experiments and CT scanning, we determined 0.85 ?c as the key thresholds for mass release of elastic strain energy and bursting of micro-fractures. A correlation between fracture volume density and strain energy density under uniaxial stress state was developed based on the Theory of Geomechanics. Then using the combined Mohr-Coulomb criterion and Griffith's criterion and considering the effect of filling degree in fractures, we continued to modify and deduce the mechanical models of fracture parameters under complex stress states. Finally, all the geomechanical equations were loaded into the finite element (FE) platform to quantitatively simulate the present-day 3-D distributions of fracture density, aperture, porosity, permeability and occurrence based on paleostructure restoration of the Keshen anticline. Its predictions agreed well with in-situ core observations and formation micro-imaging (FMI) interpretations. The prediction results of permeability were basically consistent with the unobstructed flow distributions before and after the reservoir reformation.

SUBMITTER: Feng J

PROVIDER: S-EPMC6221284 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An improved geomechanical model for the prediction of fracture generation and distribution in brittle reservoirs.

Feng Jianwei J Li Li L Jin Jianli J Dai Junsheng J Luo Peng P

PloS one 20181107 11

It is generally difficult to predict fractures of low-permeability reservoirs under high confining pressures by data statistical method and simplified strain energy density method. In order to establish a series of geomechanical models for the prediction of multi-scale fractures in brittle tight sandstones, firstly, through a series of rock mechanics experiments and CT scanning, we determined 0.85 σc as the key thresholds for mass release of elastic strain energy and bursting of micro-fractures. ...[more]

PMID: 30403681

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Project description:BackgroundAccurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction.MethodsWe examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors.ResultsA standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072.ConclusionsWe generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

Dataset Information

An improved geomechanical model for the prediction of fracture generation and distribution in brittle reservoirs.

Publications

An improved geomechanical model for the prediction of fracture generation and distribution in brittle reservoirs.

Similar Datasets

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