查看更多>>摘要：The size, shape, and connection of pores in a coal seam after hydraulic fracturing determines coalbed methane (CBM) production. To determine the variation characteristics of micro-nano-scale pores in a CBM reservoir, hydraulic fracturing simulation experiments were conducted using Sihe and Chengzhuang coal samples. The pore structures of the coal before and after hydraulic fracturing were studied using scanning electron microscopy (SEM), liquid nitrogen absorption (LNA), and mercury intrusion porosity (MIP) measurements. After hydraulic fracturing, the number of mesopores increased significantly, which consequently caused increases in their pore volume (PV: 20.40-479.85%) and pore specific surface area (PSSA: 58.08-2490.69%). The change characteristics of macropores were controlled by the coal's mechanical properties and in-situ stress. Owing to the different mechanical properties of coal, there were two forms of pore modification by hydraulic fracturing: pore brittle fragmentation and deformation. Coal with a larger elastic modulus and smaller Poisson's ratio was found to be prone to brittle failure, and the connectivity of pores increased significantly after hydraulic fracturing. The mercury withdrawal efficiency of coal samples increased from 14.42% to 27.01-36.94% after hydraulic fracturing. Larger in-situ stress will inhibit pore expansion during hydraulic fracturing, causing further pore compression.
Maria Rosa R.T. GoesThamires A.L. GuedesThiago d'Avila
9页
查看更多>>摘要：Black oil delumping, also known as a stream conversion method, converts a black oil wellstream into a compositional wellstream. This procedure ensures consistent flowrate allocations and monitoring of well's performance. This method requires volumetric oil and gas flowrates given in well-test reports, an equation of state model, and addidonal black oil information reported in the Well Test, PVT Analysis, and Gas Chromatographic Analysis. This work proposes an improvement on the method to convert black oil data into compositional wellstream. The method's performance was tested using data of three wells of a platform from an offshore oil field. This improvement significandy increased the accuracy of the method by decreasing the maximum percentage relative error from 16.50% to 4.44% when comparing the calculated and measured oil and gas properties for Well 1, for example. The method also preserves the gas and oil ratio reported in the well tests.
查看更多>>摘要：The minimum miscibility pressure (MMP) between CO2 and crude oil is a critical parameter for CO2 enhanced oil recovery (EOR). Whilst different methodologies have been employed to determine MMP, these methods are either time-consuming or unable to be executed in the actual rock core samples from the relevant reservoir and as such, do not directly consider any accompanying kinetic effects. Here we consider a range of nuclear magnetic resonance (NMR) measurement techniques performed on a benchtop NMR apparatus in terms of their ability to estimate MMP; specifically 1D imaging, self-diffusion measurements and T1/T2 relaxation measurements. Such MMP measurements were performed on two model oils (decane and hexadecane), allowing for validation against comparable MMP literature data, and a local crude oil sample - in this case the results were compared against a PVT measurement performed using a high-pressure variable volume cell (WC). Reasonably good agreement with these alternative sources of MMP data were realized via NMR measurements of self-diffusion; these provided consistent estimates of MMP for a wider range of oils when compared to 1D imaging and NMR relaxation measurements. NMR T2 measurements however performed equivalentiy to self-diffusion measurements for higher viscosity fluids based on the limited number of samples studied; such measurements require much simpler NMR hardware and are more readily accessible in both the laboratory and in the field.
Krishna PanthiMauricio SotomayorMatthew T. Balhoff
8页
查看更多>>摘要：Development of surfactant formulations for high temperature (100 °C), high salinity (>50,000 ppm) and especially high hardness (>2000 ppm) reservoirs is challenging. Alkah-surfactant-polymer processes have been developed in the past, but requires soft injection brine. The objective of this work is to develop a surfactant-polymer (SP) process for a high temperature, high salinity (HTHS) reservoir that can be injected with available hard brines (such as sea water) to achieve ultra-low IFT, low residual oil saturation, and low surfactant retention in carbonate rocks. Phase behavior experiments were performed to identify a combination of anionic and zwitterionic surfactants. Four tertiary chemical corefloods were conducted in Indiana Limestone cores to test oil recovery and surfactant retention. A copolymer containing acrylamido-tertiary-butyl sulfonate, SAV10xv was identified to be stable at this HTHS condition. Sodium polyacrylate (NaPA) was identified as a sacrificial chemical to reduce surfactant adsorption. The chemical floods were successful in reducing the residual oil saturation to 8-11%. A preflush of NaPA reduced the surfactant retention from 0.133 mg/g to 0.017-0.038 mg/g of rock. Including the NaPA in the chemical slug was not as effective. The use of this surfactant formulation with a preflush of sodium polyacrylate can enhance the oil recovery in high temperature, high salinity carbonate reservoirs with very low surfactant retention.
查看更多>>摘要：Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R~2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones.
查看更多>>摘要：Hydraulic fracturing is a commonly adopted and effective well stimulation technique in the oil and gas extraction area. Under a mixed I/III loading condition, the crack front segmentation (i.e., the parent crack segments into echelon-shaped daughter cracks) usually occurs, which highly complicates the paths of fluid-driven cracks. This paper presents an efficient numerical model for 3D hydraulic fracturing simulation considering crack from segmentation on basis of the extended finite element method (XFEM). Solutions of the momentum balance equation and the fluid flow equation are simultaneously determined by the Newton-Raphson method along with a reduction technique. In the XFEM framework, a robust local mesh-refinement scheme of the tip-enriched elements is designed to enhance the resolution of the near-front stress field which is crucial for the determination of crack segmentation and propagation behaviors. The locally refined tip-enriched elements are then divided into a series of tetrahedra to perform high-accuracy numerical integration. After verification of the proposed approach, the effects of several critical parameters in hydraulic fracturing treatments are investigated. Results show that crack front segmentation has significant effects on the resulting crack paths and crack aperture distribution. The propagation of hydraulic fractures will be depressed on account of the stress shadow induced by overlapped segments, leading to higher pumping pressure compared to the case without considering front segmentation. The sensitivity analyses indicate that larger elastic modulus of rode formation, larger fluid viscosity, higher fluid pumping rate, and smaller fluid leak-off coefficient can alleviate the influence of crack front segmentation on the pumping pressure. Larger elastic modulus, larger fluid viscosity, higher fluid pumping rate, and greater fluid leak-off coefficient lead to smaller twisting angles of the segments and smaller overlapping ratios.
查看更多>>摘要：Liquid loading is one of the main factors that can severely impede shale gas production. It is crucial for efficient operation of gas production to forecast the liquid loading in real time. Most of the existing techniques for liquid loading prediction are based on physical modeling, which are generally limited by human knowledge of the underlying mechanism of liquid loading formation. In this study, we propose a data-driven method for liquid loading prediction in shale gas wells based on deep anomaly detection technology. In particular, we employ deep neural networks to learn the normal behavior of a well and test a given sequence for anomaly by measuring the sequence reconstruction errors of the neural network with adaptive threshold. Unlike existing physical model-based methods, our method makes no assumptions about the cause of liquid loading, while the prediction results are merely determined by historical records. To verify the effectiveness of our method, we conduct experiments on real shale gas production data coming from 73 shale gas wells and the precision of method is 85.71%. The experimental results demonstrate that our method surpasses existing physical modeling based methods at prediction accuracy and timeliness.
查看更多>>摘要：Quantification of liquefied gas surface evaporation in partially filled cryogenic tank is important in both design process and operations control later. This paper focuses on effects of external heat leaks on the surface evapcration and the natural convection of a liquefied gas contained in cryogenic cylindrical tank with consideration of thermocapillarity. The evaporation mass flow rate is assumed negligible; however, the absorbed heat during the process is considered and defined according the Hashemi-Wesson model. The physical problem was formulated in dimensionless form, and then solved numerically using finite volume procedure. The external heat leaks and the surface evaporation heat flux were quantified by means of Nusselt number. Several simulations have been conducted based on the Rayleigh number (10~(+4) ≤ Ra ≤ 10~(+5)), Marangoni number (0 ≤ Ma ≤ 2000) and the cavity aspect ratio (0.5 ≤ AR ≤ 2). Results showed that as Ra increases, the heat transfer rate from wall to fluid increases as well as the evaporation rate. Large value of Ma can reduce the surface evaporation heat flux up to 5% due to thermocapillary flow. High filling level reduces surface evaporation up to 42% for AR = 0.5, whereas low filling level promotes it to 46% for AR = 1.5, with respect to AR = 1. Free surface and side wall Nusselt numbers are strongly dependent on Ra, Ma and AR, and both have been correlated in simple formulas within engineering tolerance ±5%.
Mohammad RezaeeSeyed Mojtaba Hosseini-NasabJalal Fahimpour
17页
查看更多>>摘要：One of the most important methods for increasing the oil recovery of petroleum reservoirs is gas injection However, it will not result in good oil recovery in many cases due to a low volumetric sweep efficiency. Foam flooding emerged as a promising solution for improving gas flooding. Still, there are significant concerns regarding the use of foam due to its un-stability in reservoir conditions and the vicinity of crude oil. For a foam flooding process to be successful, high-performance foaming agents must be selected to produce stable foam. One of these agents could be the use of nanoparticles. In this paper, we have attempted to create foam by making a low-cost nanoparticle, which can be obtained from an industrial waste called fly ash and addressing its impact on the crucial parameters of the foam EOR process. In the current study, fly ash nanoparticles were first obtained using a specific ball milling method and then used with a surfactant solution to form foam. Foam performance at bulk volume was evaluated, and results indicated that a small amount of fly ash nanoparticles could significantly increase the foam stability in the presence of a cationic surfactant. Foam agents also changed the surface wettability from completely oil-wet to water-wet. Furthermore, it has been observed that fly ash nanoparticles can reduce the IFT between the oil and water phase. In addition to bulk stability tests, flooding tests suggested that the nano-stabilized foam could increase the ultimate oil recovery in quasi-two-dimensional (2D) porous media tests.
查看更多>>摘要：In recent years, a dictionary learning and sparse representation based data-driven amplitude variation with offset (AVO) inversion algorithm has achieved desirable performance. However, in this data-driven inversion (DDI) method, sparse coefficients sequence generated by K-SVD may not always be convergent. Furthermore, the convergence of the data-driven AVO inversion results cannot be guaranteed. On the other hand, using L2 norm as a loss function in the DDI algorithm will lead to a robustness problem when seismic data contains outlier noise. In this paper, we propose a robust AVO inversion algorithm based on generalized nonconvex dictionary learning. The generalized nonconvex dictionary learning algorithm utilizes a family of nonconvex functions as the sparsity-inducing function for accurate estimation and a convergent sparse coefficient sequence. To deal with outlier noise, a smoothed L, norm is utilized as the loss function. Meanwhile, a new spectral Polak-Ribiere-Polyak (PRP) conjugate gradient algorithm is used to optimize the entire robust AVO inversion problem. Furthermore, the convergence analysis of the proposed algorithm is provided. In comparison with the conventional DDI algorithm, the proposed algorithm is robust, convergent, and computationally efficient. Results of synthetic data and field data experiments verify the superior performance of the proposed algorithm.
NSTL