Yunyun Hu

Through-Casing Hydraulic Fracture Evaluation by Induction Logging II: The Inversion Algorithm and Experimental Validations

Evaluation of hydraulic fractures has been under intensive study since the last decade. Among published works, only a few have included casing effects. This paper focuses on the through-casing electromagnetic (EM) induction imaging method that evaluates hydraulic fractures with enhanced contrasts. An experimental system and an inverse scattering algorithm are presented. The laboratory scaled experimental system is built for the feasibility study of the EM induction imaging method for the through-casing hydraulic fracture evaluation. To develop the inverse scattering algorithm, fractures outside boreholes with metallic casing are modeled by a novel hybrid approximation method. This method combines the distorted Born approximation and the mixed order stabilized bi-conjugate gradient fast Fourier transform method to solve the forward scattering problem. The variational Born iterative method is applied to solve the nonlinear inverse problem iteratively. Experimental results show that the inverse scattering algorithm is effective for EM contrast enhanced through-casing hydraulic fracture evaluation.

Efficient Noniterative Implicit Time-Stepping Scheme Based on E and B Fields for Sequential DG-FETD Systems

The discontinuous Galerkin finite-element time-domain (DG-FETD) method with implicit time integration has an advantage in modeling electrically fine-scale electromagnetic problems. Based on domain decomposition methods, it avoids the direct inversion of a large system matrix as in the conventional FETD method; by employing implicit time integration, it obviates an extremely small time-step interval to maintain stability as in explicit schemes. Based on curl-conforming basis functions for the electric field intensity E field and divergence-conforming basis functions for the magnetic flux density B field, a new noniterative implicit time-stepping scheme is proposed to efficiently solve sequentially ordered systems for electrically fine-scale problems. Compared with the previous EH-based scheme, the new scheme introduces fewer unknowns and, thereby, results in a smaller matrix system. Based on the Crank-Nicholson algorithm for time integration, the matrix system is in a block tridiagonal form. Then, through separating the surface unknowns from the volume unknowns, a block lower-diagonal-upper (LDU) decomposition is implemented, reducing the computational complexity of the original system. The adaptivity of parallel computing in subdomain level during preprocessing further helps shorten the computation time. Numerical results confirm that the proposed LDU scheme presents improved efficiency in terms of memory and CPU time while retaining the same accuracy, compared with the previous implicit block-Thomas method. With respect to the explicit Runge-Kutta method and the standard FDTD, it also shows an advantage in CPU time. The proposed scheme will help improve the performance of DG-FETD in modeling electrically fine-scale problems.

Multiphysics Coupling of Dynamic Fluid Flow and Electromagnetic Fields for Subsurface Sensing

Summary form only given. Electromagnetic (EM) measurement has been extensively applied in subsurface sensing while fluid flow modeling is capable of characterizing subsurface fluid flow behavior. The multiphysics coupling of the EM measurement and dynamic fluid flow analysis has significant potential to improve electromagnetic geophysical exploration with injecting electromagnetic contrast agents.

Application of BCGS-FFT and distorted born approximation for hydraulic fracturing detection and imaging

With the ever increasing number of research on hydraulic fracture aiming at improved oil production, forward and inverse solvers based on electromagnetic method to detect and reveal properties of hydraulic fracture have become an important subject of research. Most of existing forward and inverse methods are developed to simulate the well logging model, such as Method of Moments (MoM) and Born Approximation. Those methods have the advantages to reconstruct the geometrical and electromagnetic information of formation. However, they are not fast enough and the memory cost are large. Moreover, when those methods are used to simulate hydraulic fractures, they are not able to obtain the accurate result.

Enhanced subsurface sensing with nanoparticles as contrast agents for oil industry

In recent years, the applications of nanotechnology have been extensively researched in many key areas of oil industry, such as exploration, drilling and production. Nanomaterials can be developed as excellent imaging-contrast agents in their magnetic and electric properties. They are able to flush with the injection fluids through the reservoir micro-size pores. Tracing these contrast agents with electromagnetic tomography technology can potentially help tracking the flood-front in waterflood, monitoring enhanced oil recovery process and field characterization.

Contrast enhanced through casing hydraulic fractures mapping

Hydraulic fracturing is being performed in more than 60 years in more than a million wells and counting. Despite the long history in hydraulic fracturing, the growth of fractures over time is not well understood. The creation of hydraulic fractures can be monitored in real time via micro-seismic method. However, this method is only effective during fracturing process. After hydraulic fractures are created, the growth of fractures remains unknown. There is a lack of methods to effectively characterize fractures in the post fracturing period.

Multiphysics coupling of dynamic fluid flow and electromagnetic fields for subsurface sensing

Nanoparticles designed with high electric conductivity and magnetic permeability are injected into oil reservoirs to enhance fluid flow monitoring. The movement of nanoparticles with the flow in a porous medium can be modeled by solving the flow transport equation. In this research, the three-dimensional spectral-element time-domain method based on Gauss-Lobatto-Legendre polynomials is employed to solve the fluid flow equation to obtain the nanoparticle (NP) concentration distribution in reservoirs. This method shows spectral accuracy, as the error decreases exponentially with the order of basis functions. The injected fluid with high contrast NPs increases the electric conductivity and magnetic permeability in the flooded zone, thus enhancing the electromagnetic (EM) signals in the receivers. Based on the coupling of the dynamic fluid flow and crosswell EM measurement, we are able to analyze the detection range of EM sensing with the high contrast NPs injection. The EM responses with different types of NPs injection are investigated under both an electric dipole and a magnetic dipole. The magnetic contrast NPs excited by a magnetic dipole source can generate a detectable signal while the electric contrast NPs can generate a detectable signal when excited by an electric dipole. The EM response of an inhomogeneous formation with a low permeable region shows that the signal at the producer near the barrier is lower than the other producers. The proposed multiphysics coupling technique of fluid flow and EM measurements can provide guidance for NPs field application and help monitor the flow movement in reservoirs.