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Shale gas resources (SGR), as a representative of natural gas hydrate reservoirs, have been the main energy supply for the energy consumption currently. The multi-scale pore structure of shale, complicated seepage mechanisms, including Knudsen diffusion, matrix deformation, stress sensitivity, non-Darcy flow and spatial fracture network stimulated by hydraulic fracturing technology have posed huge challenges to an accurate prediction and assessment of shale gas recovery. A full understanding of gas seepage mechanism of shale gas is the critical and scientific issue to develop carbon hydrogen energy resources effectively. It is very urgent to establish a comprehensive mathematical model to analyze the productivity capacity through simultaneously considering various flow mechanisms and fractures network system. To fill this gap, this paper presents a comprehensive numerical model of hydraulic fracturing horizontal well with discrete fracture network where embedded discrete fracture model (EDFM) is employed to characterize the coupled phenomenon between discrete fracture network and fractured SGR. And then two numerical discretization methods, e.g., finite difference and finite-volume, are used to numerically discretize the equations, subsequently, the Newton-Raphson iterative method is adopted to obtain the final solutions. Finally, the sensitivity analysis experiments are employed to investigate the effects of the key parameters. The results can provide some certain guidance for the optimization of stimulated treatment in natural gas hydrate reservoirs.

Understanding and manipulating wettability alterations has tremendous implications in theoretical research and industrial applications. This study proposes a novel idea of applying ultrasonic for wettability alterations and also provides its quantitative characterizations and in-depth analyses. More specifically, with pretreatment of ultrasonic, mechanisms of wettability alteration were characterized from the contact angle measurements, as well as the in-depth analyses from atomic force microscopy (AFM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). After ultrasonic treatments, the wettability of mineral with low permeability is determined to altered from strong hydrophilic to intermediate wettability. The mechanism interpretations are conducted by means of the AFM, XRD, and FTIR. Basically, as the time of ultrasonic treatment increases, the AFM results indicate that the roughness of rock surface and oil/rock interface (contact area) with surroundings of brine is enhanced. Meanwhile, the XRD results show the diffusions of clays from the rock surface to the aqueous phase, and FTIR indicates that the number of functional groups of Si-O-Si, C-O-C, C-O, C═O, and OH decreases while the number of COOH and C═C═O groups increases. This study clearly reveals the surface chemistry of oil-rock wettability alteration in the subsurface conditions, which would provide technical support for subsurface usage of geo-energy productions and carbon sequestrations.

It is complex and obviously different for the production characteristics of CO2 water-alternating-gas (WAG) flooding in tight reservoir and influenced by quite a few factors. Therefore, the prediction of oil production is a key matter of efficient development of CO2 WAG to be solved in tight reservoirs. In order to cope with this issue, in this paper, the production characteristics of CO2 WAG flooding are analyzed and classified in tight oil reservoir of block A as an example. On this basis, properties of reservoir, fracture factors, and operational factors are taken into account and the sensitivity of the influencing factors is carried out. Subsequently, the gray relation analysis is used to confirm the primary influencing factors. Finally, the evaluated model is established to predict oil production rapidly. The results illustrate that the wells of CO2 WAG flooding in tight reservoirs can be divided into four types of fluid production characteristics. The production is affected by permeability, reservoir thickness, amount of sand entering the ground, amount of liquid entering the ground, gas/water ratio, the injection rate, injection pressure, permeability variation coefficient, water sensitive index, acid sensitive index, and expulsion pressure. And the primary influencing factors are the amount of sand entering the ground, reservoir thickness, and amount of liquid entering the ground. The oil production can be predicted quickly based on the relation between production and comprehensive evaluation factor of production. The average relative error between the predicted results and the actual production is 8%, which proves the reliability and accuracy of this method.

Summary Finding multiple posterior realizations through a reservoir history-matching procedure is of significance for uncertainty quantification, risk analysis, and decision making in the course of reservoir closed-loop management. Previously, an efficient, distributed, optimization algorithm—global linear-regression (GLR) with distributed Gauss-Newton (GLR-DGN)—has been proposed in the literature to iteratively minimize multiple objective functions by performing Gauss-Newton (GN) optimizations concurrently while dynamically sharing information between dispersed regions in a reduced parameter space. However, theoretically, the required number of initial training models should be larger than the number of parameters to guarantee a unique solution for the GLR equation for sensitivity-matrix estimation. This limitation makes the large-scale reservoir history-matching problem with a large amount of parameters almost intractable. We enrich the previous history-matching framework by integrating our recently proposed smooth local parameterization (SLP) and DGN for the sensitivity-matrix calculation. Motivated by the fact that one specific flow response mainly depends on a few influential or local parameters, which can be generally identified by the physical position of the wells (e.g., the parameters in the zone surrounding the wells), which is particularly true for large-scale reservoir models, this paper presents a new integration of subdomain linear-regression (SLR) with DGN, referred to as SLR-DGN. This SLP allows us to independently represent the globally spatial parameter field within low-order parameter subspaces in each subdomain. On the basis of the SLP procedure, only a few training models are required to compute local sensitivity of the response functions using a subdomain linear regression. SLP is a linear transformation with smoothness and differentiability, which makes it particularly compatible with Newton-like gradient-based optimization algorithms. Furthermore, we also introduce an adaptive scheme, named weighting smooth local parameterization (WSLP), in which the minimization algorithm adaptively determines the weighting coefficients and the optimal domain decomposition (DD) correspondingly, to mitigate the negative effects of an inappropriate DD strategy. We support our framework with numerical experiments for a four-variable toy model and a modified version of the sensitivity analysis of the impact of geological uncertainties on production (SAIGUP) model with spatially dependent parameters. Comparisons with previous GLR-DGN have shown that our new framework can generate comparable and even better results with significantly reduced computational cost. This SLP has high scalability, because the number of training models depends primarily on the number of local parameters in each subdomain and not on the dimension of the underlying full-order model. Activating more subdomains results in fewer local parameter patterns and enables us to run fewer training models. For a large-scale case study in this work, to optimize 412 global parameters, SLR-DGN needs only 100 initial-model simulations. In comparison to GLR-DGN where the parameters are defined over the entire domain, the central-processing-unit cost is reduced by a factor of several orders of magnitude, while retaining reasonable accuracy.

Five-spot well pattern (FSWP) scheme has shown appealing potentialities to enhance the recovery of coal-bed methane (CBM) from methane hydrocarbon reservoirs (MHR). In this paper, a new framework aimed at systematically investigating productivity performance of FSWP scheme with inter-well pressure interference (IWPI) is presented. First, mathematical models which are used to characterize the fluid flow within MHR and hydraulic fractures (HFs) are separately derived. Second, Laplace transformation and Stehfest numerical algorithm are utilized to couple those two flow systems and obtain the pressure-transient solutions of FSWP scheme. Finally, pressure characteristics are discerned and sensitivity analysis of key parameters is implemented as well. This semi-analytical approach outperforms numerical simulation from the point of computational efficiency. Several common flow regimes, e.g., linear and bi-linear flow regimes, are essentially deformed induced by IWPI. Several significant parameters, including gas rate, fracture half-length, and well spacing on the occurrence of IWPI are systematically analyzed. This work gains some new knowledge about the productivity performance of FSWP scheme with the existence of IWPI when extracting CBM from methane hydrate reservoirs (MHR), which provides energy engineers considerable instructions on optimizing the development of methane hydrate reservoirs.

CO2 enhanced oil recovery (EOR) has been proven its capability to explore the unconventional tight oil reservoirs and potential for geological carbon storage. Meanwhile, the extremely low permeability pores exaggerate the difficulty CO2 EOR and geological storage processing in the actual field. This paper initiates the ultrasonic-assisted approach to facilitate the oil-gas miscibility development and finally contribute to unlock more tight oils. First, the physical properties of crude oil with and without ultrasonic treatments were experimentally analysed through gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR) and viscometer. Second, the oil-gas minimum miscibility pressures (MMPs) were measured from the slim-tube test and the miscibility developments with and without ultrasonic treatments were interpreted from the mixing-cell method. Third, the nuclear-magnetic resonance (NMR) assisted coreflood tests were conducted to physically model the recovery process in porous media and directly obtain the recovery factor. Basically, the ultrasonic treatment (40KHz and 200W for 8 hours) was found to substantially change the oil properties, with viscosity (at 60°C) reduced from 4.1 to 2.8mPa·s, contents of resin and asphaltene decreased from 27.94% and 6.03% to 14.2% and 3.79%, respectively. The FTIR spectrum shows the unsaturated C-H bond, C-O bond and C≡C bond in macromolecules were broken from ultrasonic, which caused the macromolecules (e.g., resin and asphaltenes) to be decomposed into smaller carbon-number molecules. Accordingly, the MMP was determined to be reduced from 15.8 to 14.9MPa from the slim-tube test and the oil recovery factor increased by over 10%. This study reveals the mechanisms of ultrasonic-assisted CO2 miscible EOR in producing tight oils.

Based on fractal theory (FT) and fractional calculus (FC), a new fractally fractional diffusion model (FFDM) of composite dual-porosity has been developed to evaluate performance of multiple fractured horizontal wells (MFHWs) with stimulated reservoir volume (SRV) in tight gas reservoirs (TGRs). More specifically, FT is used to characterize the complex and heterogeneous fracture network (FN) both inside and outside of SRV, while anomalous behavior of diffusion processes both inside and outside of SRV is quantified by applying the temporal fractional derivatives. The FFDM is then solved by the Laplace transformation, line source function, the numerical discrete method, and superposition principle. The transient pressure responses are then inversely converted from Laplace domain into real time domain with the Stehfest algorithm, and the FFDM is also validated, and type curves are generated as well. Flow stages are subsequently identified together with analysis on characteristics of the type curves, especially the anomalous features different with those generated from the conventional Euclidean model. Sensitivity analyses of some related parameters have also been discussed as well. And the FFDM is then also matched with the real field well-testing data of a MFHW with SRV in a TGR. The proposed FFDM provides a new understanding of the performance of MFHWs with SRV in TGRs, which can be used to interpret the field pressure data more accurately and appropriately.

In this paper, the equilibrium two-phase compositions are predicted and analyzed to elucidate the pressure dependence of the equilibrium interfacial tensions (IFTs) of three different light crude oil–CO2 systems. First, three series of the dynamic IFT tests for a dead light crude oil–pure CO2 system, a live light crude oil–pure CO2 system, and a dead light crude oil–impure CO2 system at different equilibrium pressures from the literature are used. Second, the modified Peng–Robinson equation of state (PR-EOS) is tuned by using measured pressure–volume–temperature (PVT) data to predict the equilibrium two-phase compositions of the three light crude oil–CO2 systems. Such tuned PR-EOS together with the parachor model is applied to predict the equilibrium IFTs, which are compared with and validated by the measured IFT data. Third, the pressure dependence of the equilibrium IFTs, the initial oil and gas composition effects, and the initial gas fraction effect are examined. The density difference between the light crude oil and gas phase is found to be a key factor in the parachor model for the IFT predictions. The equilibrium IFT vs. pressure curve is found to have three different pressure ranges, which correspond well to those for the density difference. Moreover, the initial oil and gas compositions affect the equilibrium two-phase compositions and IFTs to different extents. The live light crude oil–pure CO2 equilibrium IFT is reduced with an increased initial gas fraction. For the dead light crude oil–pure/impure CO2 system, the miscibility with zero IFT can be achieved only if the initial gas phase has more than 0.70 mol fraction. Otherwise, it is the complete gas dissolution into the light crude oil that leads to zero IFT.

In this paper, a generalized and pragmatic framework has been developed to optimize the vector well pattern in tight sandstone reservoirs. More specifically, the maximum principal formation stress, natural fracture orientation, and principal permeability orientation are identified as the three key factors associated with well productivity, while both micro-elements of fractures are summed up and coefficients of the same fracture are combined according to the angle between the principal permeability orientation and direction of the hydraulic fractures to form an equation matrix. The sensitivity analysis is then conducted to examine effect of the maximum horizontal principal stress and the principal permeability on horizontal well production while determining the optimal orientation of the horizontal wellbore. Subsequently, theoretical calculation is performed to design and optimize the well pattern as well as fracture properties. It is found that the orientation of a horizontal wellbore should be perpendicular to the direction of the maximum horizontal principal stress if the angle (θ) between the orientation of the principal permeability k x and the direction of the maximum horizontal principal stress is less than 30 ° . If 30 ° ≤ θ ≤ 60 ° , the orientation of the horizontal wellbore should be perpendicular to the direction of the maximum horizontal principal stress for a weak anisotropic reservoir k x / k y 2 , while it should be perpendicular to the orientation of principal permeability k x for a strong anisotropic reservoir k x / k y ≥ 2 . The orientation of a horizontal wellbore needs to be perpendicular to the orientation of the principal permeability (kx) if 60 ° ≤ θ ≤ 90 ° . In addition to improving the recovery factor compared with that of the conventional well spacing, it is found from a field application that the optimized vector well pattern can effectively extend the stable production period, reduce water-cut, increase oil recovery up to 4.5%, and improve the overall reservoir performance.

Carbon dioxide miscible flooding has been proven to be one of the most effective enhanced oil recovery (EOR) technologies, particularly for light and medium oil reservoirs. However, specific effects of pore structure on CO2 miscible flooding recovery in low permeability reservoir lack in-depth understandings. In this paper, pore structures are specifically studied by means of the molecular mechanics to evaluate their effects on the CO2 EOR in the low permeability reservoir. First, a series lab experiments are performed for the pore and fluid characterization. More specifically, the pore throat size and distribution frequency are measured from the high-pressure mercury injection and nuclear magnetic resonance. The minimum miscibility pressure is determined from the slim-tube tests with known oil compositions tested from gas chromatography analysis. Second, the regularity of CO2 extraction is explored on the basis of molecular mechanics and the thickness of raffinate is calculated. Finally, the raffinate volume and recovery ratio in the pores are calculated after the CO2 miscible flooding. The results show that a raffinate-layer with thickness of 0.13 μm remains on the surface of the pore after the CO2 miscible flooding, which would cause the oil to be immobile since the throat could be blocked when the throat radius is smaller than 0.26 μm. The recoveries of cores C-1 and C-2 are 70.1 % and 61.4 % from calculations and 68.4 % and 59.8 % from experiments, whose errors are 2.5 % and 2.7 %, respectively. This study would be beneficial to analyze the CO2 miscible flooding in reservoirs with different pore structures and provide technical support for improving CO2 utilization efficiency.