Hybrid Data/Mechanics Based Stochastic Subsurface Characterization

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Subsurface characterization underpins many decisions made at every stage of subsurface resources utilization, such as geothermal systems and hydrocarbon exploitation. Nonetheless, such a task is a challenge and affected by inevitable sources of uncertainties, making decisions related to the subsurface highly complicated. Therefore, for the purpose of making well-informed decisions, stochastic subsurface characterization is ideally sought. In this work, we develop a set of novel hybrid stochastic subsurface characterization tools based on field data and mechanics. The work consists of three major components: (1) a probabilistic geologic layer identification model: GLI, (2) a stochastic discrete fracture network (DFN) model: EllipFrac and (3) a stochastic fracture initiation and propagation model – FracProp. Two tools were developed for GLI: (a) Artificial neural networks (ANN) based and (b) optimized multivariate Gaussian distributions based. The former method consists of a novel approach to convert the ANN decisions from deterministic to probabilistic ones, focusing on quantifying interpolation uncertainties. The latter tool targets uncertainties related to the interpolation and to the locations of the interfaces between geologic layers. Both tools are demonstrated through an application to the characterization of the subsurface of Masdar city, UAE. They are used to identify geologic layers and to create hazard maps. The remaining set of tools focuses on rock fracture characterization. The first one is EllipFrac, a stochastic DFN developed to represent natural fracture networks. EllipFrac is based on site-specific data and uses a series of stochastic processes to represent orientations, sizes, and locations of fractures and represent natural rock fracture systems. EllipFrac is demonstrated through an application to the Yates oil field, Texas, USA. Finally, FracProp, a mechanics based stochastic model to predict the initiation and propagation of induced fractures using compounded directional probability density functions. FracProp is qualitatively validated on experiments conducted at the rock mechanics lab at MIT and the simulated fractures are in agreement with the test results.

روابط وملفات

التعريف والنوع

رقم الوثيقة

fa51f04f-3d1c-422a-b4a1-88b063c13f43

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل دكتوراة

نوع الملف

pdf text

أسماء الملفات

2283267_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Herbert Einstein","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Mohammad Burhan Abdulla","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Rita Sousa","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Hybrid Data/Mechanics Based Stochastic Subsurface Characterization

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

subsurface; stochastic rock fractures networks; induced fractures; uncertainty quantification

إشراف وإعداد

الإشراف

Herbert Einstein, Rita Sousa

الإعداد

Mohammad Burhan Abdulla

الاقتباسات الببليوغرافية