Mechanistic Modeling of Distributed Strain Sensing DSS and Distributed Acoustic Sensing DAS to Assist Machine Learning Schemes Interpreting Unconventional Reservoir Datasets

Abstract

The use of fiber optics in reservoir surveillance is bringing valuable insights to fracture geometry and fracture-hit identification, stage communication and perforation cluster fluid distribution in many hydraulic fracturing processes. However, given the complexity associated with field data, its interpretation is a major challenge faced by engineers and geoscientists. In this work, we propose to generate Distributed Strain/Acoustic Sensing (DSS/DAS) synthetic data of a cross-well fiber deployment that incorporate the physics governing hydraulic fracturing treatments. Our forward modeling is accurate enough to be reliably used in tandem with data-driven (machine learning) interpretation methods. The forward modeling is based on analytical and numerical solutions. The analytical solution is developed integrating two models: 2D fracture (e.g. Khristianovic-Geertsma-de Klerk known as KGD) and induced stress (e.g. Sneddon, 1946). DSS is estimated using the plane strain approach that combines calculated stresses and rock properties (e.g. Young's modulus and Poisson ratio). On the other hand, the numerical solution is implemented using the Displacement Discontinuity Method (DDM), a type of Boundary Element Method (BEM), with net pressure and/or shear stress as boundary condition. In this case, fiber gauge length concept is incorporated deriving displacement (i.e. DDM output) in space to obtain DSS values. In both methods DAS is estimated by the differentiation of DSS in time. The analytical technique considers a single fracture opening and is used in a sensitivity analysis to evaluate the impact that rock/fluid parameters can promote on strain time histories. Moreover, advanced cases including multiple fractures failing in tensile or shear mode are simulated applying the numerical technique. Results indicate that our models are able to capture typical characteristics present in field data: heart-shaped pattern from a fracture approaching the fiber, stress shadow and fracture hits. In particular, the numerical methodology captures relevant phenomenon associated with hydraulic and natural fractures interaction, and provides a solid foundation for generating accurate and rich synthetic data that can be used to support a physics-based machine learning interpretation framework. The developed forward modeling, when embedded in a classification or regression artificial intelligence framework, will be an important tool adding substantial insights related to field fracture systems that ultimately can lead to production optimization. Also, the development of specific packages (commercial or otherwise) that explicitly model both DSS and DAS, incorporating the impact of fracture opening and slippage on strain and strain rate, is still in its infancy. This paper is novel in this regard and opens up new avenues of research and applications of synthetic DAS/DSS in hydraulic fracturing processes.