Hydraulic Tomography: 3D Hydraulic Conductivity, Fracture Network, and Connectivity in Mudstone

Abstract

We present the first demonstration of hydraulic tomography (HT) to estimate the three‐dimensional (3D) hydraulic conductivity (K) distribution of a fractured aquifer at high‐resolution field scale (HRFS), including the fracture network and connectivity through it. We invert drawdown data collected from packer‐isolated borehole intervals during 42 pumping tests in a wellfield at the former Naval Air Warfare Center, West Trenton, New Jersey, in the Newark Basin. Five additional tests were reserved for a quality check of HT results. We used an equivalent porous medium forward model and geostatistical inversion to estimate 3D K at high resolution (K blocks <1 m3), using no strict assumptions about K variability or fracture statistics. The resulting 3D K estimate ranges from approximately 0.1 (highest‐K fractures) to approximately 10−13 m/s (unfractured mudstone). Important estimated features include: (1) a highly fractured zone (HFZ) consisting of a sequence of high‐K bedding‐plane fractures; (2) a low‐K zone that disrupts the HFZ; (3) several secondary fractures of limited extent; and (4) regions of very low‐K rock matrix. The 3D K estimate explains complex drawdown behavior observed in the field. Drawdown tracing and particle tracking simulations reveal a 3D fracture network within the estimated K distribution, and connectivity routes through the network. Model fit is best in the shallower part of the wellfield, with high density of observations and tests. The capabilities of HT demonstrated for 3D fractured aquifer characterization at HRFS may support improved in situ remediation for contaminant source zones, and applications in mining, repository assessment, or geotechnical engineering.

Numerical Simulation of the Influence of Natural Fractures on Hydraulic Fracture Propagation

“…However, the process of the numerical simulation depends on the increment step, not the time. Therefore, much more dependent variable, such as water 10 Geofluids pressure, length, and width of the hydraulic fracture and the like, which regard time as independent variable, cannot be obtained.…”

“…The propagation of hydraulic fractures plays a key role in the optimisation of hydraulic fracture design and represents a challenging problem corresponding to the theoretical study of hydraulic fractures [6][7][8][9]. However, in the case of fractured reservoirs, the presence of natural fractures can change the path of the hydraulic fracture propagation, leading to the formation of a complicated fracture propagation system with multibranch fractures, which increases the complexity of the hydraulic fracture network [10,11]. Therefore, the accurate prediction and control of the hydraulic fracture morphology in fractured reservoirs is critical to improve the oil and gas production in fractured reservoirs [12][13][14][15][16].…”

Individual and joint inversion of head and flux data by geostatistical hydraulic tomography

“…Moreover, if one could measure the head and flux data at the same location, by doubling the number of observations, the Y-field estimate would be significantly improved. The potential application of FO cables for pressure measurement is discussed by Butler et al (1999) and a recent application for drawdown measurements during pumping tests is demonstrated by Tiedeman and Barrash (2020). A proper setting for the collection of head and flux data could include head measurements at boreholes while taking flux measurements at other locations using the FO-DTS technique.…”

A Quasi‐Newton Reformulated Geostatistical Approach on Reduced Dimensions for Large‐Dimensional Inverse Problems

“…Estimation of spatially variable parameter fields, such as hydraulic conductivity or transmissivity, is an essential task in groundwater flow and transport modeling. Due to costly collection and sampling of local‐scale cores, field‐scale characterization is typically implemented by inverse modeling of indirect measurements from large‐scale aquifer tests, such as pumping and tracer tests (Bui‐Thanh & Girolami, 2014; Cardiff & Barrash, 2011; Cardiff et al., 2009; Carrera & Neuman, 1986a; Cui et al., 2011; Fienen et al., 2006; Liao & Cirpka, 2011; Tiedeman & Barrash, 2020; Xu & Gómez‐Hernández, 2018; Yeh & Liu, 2000; Zhao et al., 2018; Zhu & Yeh, 2005). Such inverse problems can be conveniently addressed in a Bayesian framework, which formulates the posterior distribution of unknown parameter fields by combining the likelihood function for data fitting and a regularization term, the prior that encodes spatial correlations or smoothness of the unknown fields (Carrera & Neuman, 1986a; Gavalas et al., 1976; Isaac et al., 2015; Kitanidis and Vomvoris, 1983; Liu & Kitanidis, 2011; Neuman, 1980; Rubin et al., 2010; Woodbury & Rubin, 2000).…”