Numerical Simulation Study on Miscible EOR Techniques for Improving Oil Recovery in Shale Oil Reservoirs

Shale formations in North America such as Bakken, Niobrara, and Eagle Ford have huge oil in place, 100—900 billion barrels of oil in Bakken only. However, the predicted primary recovery is still below 10%. Therefore, seeking for techniques to enhance oil recovery in these complex plays is inevitable. Although most of the previous studies in this area recommended that CO2 would be the best EOR technique to improve oil recovery in these formations, pilot tests showed that natural gases performance clearly exceeds CO2 performance in the field scale. In this paper, two different approaches have been integrated to investigate the feasibility of three different miscible gases which are CO2, lean gases, and rich gases. Firstly, numerical simulation methods of compositional models have been incorporated with local grid refinement of hydraulic fractures to mimic the performance of these miscible gases in shale reservoirs conditions. Implementation of a molecular diffusion model in the LS-LR-DK (logarithmically spaced, locally refined, and dual permeability) model has been also conducted. Secondly, different molar-diffusivity rates for miscible gases have been simulated to find the diffusivity level in the field scale by matching the performance for some EOR pilot tests which were conducted in Bakken formation of North Dakota, Montana, and South Saskatchewan. The simulated shale reservoirs scenarios confirmed that diffusion is the dominated flow among all flow regimes in these unconventional formations. Furthermore, the incremental oil recovery due to lean gases, rich gases, and CO2 gas injection confirms the predicted flow regime. The effect of diffusion implementation has been verified with both of single porosity and dual-permeability model cases. However, some of CO2 pilot tests showed a good match with the simulated cases which have low molar-diffusivity between the injected CO2 and the formation oil. Accordingly, the rich and lean gases have shown a better performance to enhance oil recovery in these tight formations. However, rich gases need long soaking periods, and lean gases need large volumes to be injected for more successful results. Furthermore, the number of huff-n-puff cycles has a little effect on the all injected gases performance; however, the soaking period has a significant effect. This research project demonstrated how to select the best type of miscible gases to enhance oil recovery in unconventional reservoirs according to the field-candidate conditions and operating parameters. Finally, the reasons beyond the success of natural gases and failure of CO2 in the pilot tests have been physically and numerically discussed

A Systematic Design Approach For Bulk Gel Treatments Based On Gel Volume-concentration Ratio In Field Projects

Controlling excessive water production in mature oil fields has always been a desired objective of the oil and gas industry. This objective calls for planning of more effective water-control gel treatments with optimized designs to obtain more attractive outcomes. Unfortunately, planning such effective treatments remains a dilemma for reservoir engineers due to the lack of methodical design tools in the industry. This paper presents a novel systematic design approach for polyacrylamide-based bulk gel treatments by classifying their field projects according to the gel volume-concentration ratio (VCR) into three design types. In terms of one another, the approach estimates either the gel volume or the gel concentration based on the average gel VCR of each design and formation type. First, field data was collected from SPE papers and reports of US Department of Energy for 65 gel projects conducted between 1985 and 2020. Stacked histograms were then used to examine distributions of field projects according to the gel VCR and the formation type. A comprehensive review of channeling strength indicators in field gel projects was performed to identify the classification criterion and design types of gel treatments. Based on the mean-per-group concept, the average gel VCR was assessed for each design type and formation type to build the design approach. Approximations for the overall gel concentration and correlations for extremum designs were established and included in the approach. The study showed that the gel VCR is a superior design criterion for in-situ forming bulk gel treatments. It aggregates gel treatments into three project groups and ranks them according to the channeling strength. The three project groups have clear separating VCR intervals (3 bbl/ppm) and each of them is mostly dominated by one formation type. The VCR range of each project group represents one design type of the bulk gel treatments. The channeling type is the criterion of grouping and group-wise ranking of gel projects with respect to the gel VCR. In design type I, VCRs/ppm are used to treat pipe-like channeling usually exhibited by unconsolidated sandstones. More balanced VCRs of 1–3 bbl/ppm are designed for fracture-channeling frequently presented in naturally-fractured formations (design type II). Large gel treatments with VCR\u3e3 bbl/ppm are performed to address matrix-channeling often shown in matrix-rock formations (design type III). Prediction results demonstrated that the VCR approach reasonably estimates volumes and concentrations of both single gel treatments and averaged field projects in training and validation samples. Besides its novelty, the new approach is systematic, accurate, practical, and will facilitate the optimization of future gel treatments to improve their performances and success rate

Study of Gas Production from Shale Reservoirs with Multi-Stage Hydraulic Fracturing Horizontal Well Considering Multiple Transport Mechanisms

Development of unconventional shale gas reservoirs (SGRs) has been boosted by the advancements in two key technologies: horizontal drilling and multi-stage hydraulic fracturing. A large number of multi-stage fractured horizontal wells (MsFHW) have been drilled to enhance reservoir production performance. Gas flow in SGRs is a multi-mechanism process, including: desorption, diffusion, and non-Darcy flow. The productivity of the SGRs with MsFHW is influenced by both reservoir conditions and hydraulic fracture properties. However, rare simulation work has been conducted for multi-stage hydraulic fractured SGRs. Most of them use well testing methods, which have too many unrealistic simplifications and assumptions. Also, no systematical work has been conducted considering all reasonable transport mechanisms. And there are very few works on sensitivity studies of uncertain parameters using real parameter ranges. Hence, a detailed and systematic study of reservoir simulation with MsFHW is still necessary. In this paper, a dual porosity model was constructed to estimate the effect of parameters on shale gas production with MsFHW. The simulation model was verified with the available field data from the Barnett Shale. The following mechanisms have been considered in this model: viscous flow, slip flow, Knudsen diffusion, and gas desorption. Langmuir isotherm was used to simulate the gas desorption process. Sensitivity analysis on SGRs\u27 production performance with MsFHW has been conducted. Parameters influencing shale gas production were classified into two categories: reservoir parameters including matrix permeability, matrix porosity; and hydraulic fracture parameters including hydraulic fracture spacing, and fracture half-length. Typical ranges of matrix parameters have been reviewed. Sensitivity analysis have been conducted to analyze the effect of the above factors on the production performance of SGRs. Through comparison, it can be found that hydraulic fracture parameters are more sensitive compared with reservoir parameters. And reservoirs parameters mainly affect the later production period. However, the hydraulic fracture parameters have a significant effect on gas production from the early period. The results of this study can be used to improve the efficiency of history matching process. Also, it can contribute to the design and optimization of hydraulic fracture treatment design in unconventional SGRs

Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning

Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains a better clustering result by removing the differences in treating categorical and numeric features. The advantages of doing this are demonstrated with several real-world data sets with ground truth, including heart disease, teaching assistant evaluation, and credit approval. The approach is also demonstrated on noisy, mixed-type petroleum industry data. UFLA is compared with several alternative methods. To the best of our knowledge, this is the first time UFL has been extended to accomplish the fusion of mixed data types

Decoupling the Stationary Navier-Stokes-Darcy System with the Beavers-Joseph-Saffman Interface Condition

This paper proposes a domain decomposition method for the coupled stationary Navier-Stokes and Darcy equations with the Beavers-Joseph-Saffman interface condition in order to improve the efficiency of the finite element method. The physical interface conditions are directly utilized to construct the boundary conditions on the interface and then decouple the Navier-Stokes and Darcy equations. Newton iteration will be used to deal with the nonlinear systems. Numerical results are presented to illustrate the features of the proposed method

Improving Database Quality through Eliminating Duplicate Records

Redundant or duplicate data are the most troublesome problem in database management and applications. Approximate field matching is the key solution to resolve the problem by identifying semantically equivalent string values in syntactically different representations. This paper considers token-based solutions and proposes a general field matching framework to generalize the field matching problem in different domains. By introducing a concept of String Matching Points (SMP) in string comparison, string matching accuracy and efficiency are improved, compared with other commonly-applied field matching algorithms. The paper discusses the development of field matching algorithms from the developed general framework. The framework and corresponding algorithm are tested on a public data set of the NASA publication abstract database. The approach can be applied to address the similar problems in other databases

A Novel Numerical Model of Gelant Inaccessible Pore Volume for in Situ Gel Treatment

Inaccessible pore volume (IAPV) can have an important impact on the placement of gelant during in situ gel treatment for conformance control. Previously, IAPV was considered to be a constant factor in simulators, yet it lacked dynamic characterization. This paper proposes a numerical simulation model of IAPV. The model was derived based on the theoretical hydrodynamic model of gelant molecules. The model considers both static features, such as gelant and formation properties, and dynamic features, such as gelant rheology and retention. To validate our model, we collected IAPV from 64 experiments and the results showed that our model fit moderately into these lab results, which proved the robustness of our model. The results of the sensitivity test showed that, considering rheology and retention, IAPV in the matrix dramatically increased when flow velocity and gelant concentration increased, but IAPV in the fracture maintained a low value. Finally, the results of the penetration degree showed that the high IAPV in the matrix greatly benefited gelant placement near the wellbore situation with a high flow velocity and gelant concentration. By considering dynamic features, this new numerical model can be applied in future integral reservoir simulators to better predict the gelant placement of in situ gel treatment for conformance control

Fabrications and Applications of Micro/nanofluidics in Oil and Gas Recovery: A Comprehensive Review

Understanding fluid flow characteristics in porous medium, which determines the development of oil and gas oilfields, has been a significant research subject for decades. Although using core samples is still essential, micro/nanofluidics have been attracting increasing attention in oil recovery fields since it offers direct visualization and quantification of fluid flow at the pore level. This work provides the latest techniques and development history of micro/nanofluidics in oil and gas recovery by summarizing and discussing the fabrication methods, materials and corresponding applications. Compared with other reviews of micro/nanofluidics, this comprehensive review is in the perspective of solving specific issues in oil and gas industry, including fluid characterization, multiphase fluid flow, enhanced oil recovery mechanisms, and fluid flow in nano-scale porous media of unconventional reservoirs, by covering most of the representative visible studies using micro/nanomodels. Finally, we present the challenges of applying micro/nanomodels and future research directions based on the work

A Comprehensive Review of Experimental Evaluation Methods and Results of Polymer Micro/nanogels for Enhanced Oil Recovery and Reduced Water Production

In recent years, polymer micro/nanogels which are re-crosslinked polymers with 3D networks, have attracted a lot of interest in Enhanced Oil Recovery (EOR) field. In size of micro/nanometers, these gel particles are designed to be conformance control agents for in-depth fluid diversion, and various experimental research have been undertaken to investigate the possibilities of applying micro/nanogels in oilfield. However, it is still unclear that how to utilize micro/nanogels to their full potential in oilfield because the transport mechanisms and EOR mechanisms of micro/nanogels are not well studied currently. By reviewing experimental evaluations and corresponding results of micro/nanogels, including evaluation of particle physiochemical properties, transport, and potential EOR mechanisms, the review aims to discuss the evaluation of micro/nanogel particles, transport issue in many experimental designs and the debates of EOR mechanisms. Finally, we present the current challenges of micro/nanogels application and recommend the future research directions based on the review

Pattern Recognition for Steam Flooding Field Applications based on Hierarchical Clustering and Principal Component Analysis

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward\u27s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance