Multiscale Digital Porous Rock Reconstruction Using Template Matching

Abstract

Rocks are heterogeneous multiscale porous media: two rock samples with identical bulk properties can vary widely in microstructure. The advent of digital rock technology and modern 3‐D printing provides new opportunities to replicate rocks. However, the inherent trade‐off between imaging resolution and sample size limits the scales over which microstructure and macrostructure can be identified and related to each other. Here, we develop a multiscale digital rock construction strategy by combining X‐ray computed microtomography and focused‐ion beam (FIB)‐scanning electron microscope (SEM) images, and we apply the technique to a tight sandstone. The computed tomography (CT) scanning images characterize macroscale pore structures, while the FIB‐SEM images capture microscale pore textures. The FIB‐SEM images are then coupled to CT images via a template‐matching algorithm and superposition. Bulk properties, including porosity and pore and throat size distribution, can be recovered with this approach. Permeability prediction with a pore network model for the largest connected pore network are 3 orders and 1 order of magnitude greater than the bulk rock measured value using the CT‐only and the SEM‐CT coupled images, respectively.

Supplementary energy development boundaries of staged fracturing horizontal wells in tight oil reservoirs

“…An economic optimization model (1) for parameters of horizontal well hydraulic fractures is presented in the document, Horizontal well fracturing optimization design technology (Jiao, 2001; Liu, 2000; Wu and Zhao, 2005; Zhang, 2001). The optimized fracture length is as shown in equation (2) where NPV is the net present value; R n is the income of crude oil in n years (×10 4 RMB); N is the total production time; COST W is the cost of drilling (×10 4 RMB); COST T is the total cost of fracturing (×10 4 RMB); COST F is the cost including construction overhead, auxiliary equipment cost, cost of labor (×10 4 RMB); COST HH is the cost obtained with the number of fracturing pump vehicles multiplied by a single unit cost (×10 4 RMB); COST LP is the expenses on fracturing fluid and propping agent required for a unit fracture length (×10 4 RMB); L f is the fracture length (m); N f is the number of fractures; i is the discount rate (%); and the current oil price is $50/barrel.…”

“…Currently, tight oil is a relatively realistic substitute for the conventional hydrocarbon resources in the future. However, tight reservoirs are usually characterized by small pore throats, low permeability, large seepage resistance, and low single well production, and the effective development rates of the tight oil reservoirs are low (Lin et al, 2018(Lin et al, , 2019Shanley and Cluff, 2015;Wang et al, 2018). In order to improve the development efficiency, the staged fracturing horizontal well technology emerged Jamiolahmady, 2018a, 2018b;Shen et al, 2018;Yu et al, 2017).…”

Developing a new technique for the simultaneous optimization of both segmented time and injection rate for water flooding reservoirs: An analysis of Shengli Oilfield XIN-42 reservoir block

“…With the development of digitized and intelligent oilfields, optimization methods for oil extraction are currently receiving significant attention (Aitokhuehi and Durlofsky, 2005; Brouwer and Jansen, 2002; Brouwer et al., 2004; Hasan et al., 2009; Izadmehr et al., 2018; Jansen et al., 2009; Lin et al., 2019; Oliveira and Reynolds, 2014). The adjustment and control of water injection or fluid production rates is a primary method of increasing oil production.…”

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

“…These above systems have made great progress on the basis of traditional Markovian jump systems. Apart from modeling abrupt changes in systems, the Markov process can also be applied to the scope of digital core . In addition, many control methods for time‐delayed systems with Markovian jumping parameters have been proposed, including H ∞ control, adaptive control, dissipative control, finite‐time control, synchronization control, and asynchronous control …”

“…Apart from modeling abrupt changes in systems, the Markov process can also be applied to the scope of digital core. 10,11 In addition, many control methods for time-delayed systems with Markovian jumping parameters have been proposed, including H ∞ control, 12,13 adaptive control, 14,15 dissipative control, 16,17 finite-time control, 18,19 synchronization control, 20,21 and asynchronous control. 22,23 At the same time, the concept of asynchronization is really contrary to synchronization.…”