查看更多>>摘要：In the present study, molecular characteristics of type-II oil shale kerogen of Chang 7 in Changqing Oilfield in Ordos Basin are acquired by pyrolysis-gas chromatography-mass spectroscopy (Py-GCMS), solid-state ~(13)C nuclear magnetic resonance (~(13)C NMR) and X-ray photoelectron spectroscopy (XPS). Its pyrolysis mechanism is investigated by reactive force field molecular dynamics at 2000-3000 K. The optimal temperature for oil and organic gas yield of Chang 7 type-II oil shale kerogen are obtained. Bond breakage sites for Chang 7 type-II oil shale kerogen are proposed. Cross-linking at the optimal temperature is investigated. Formation mechanism of typical gas molecules is revealed by explaining HS and HO radical competition. The results show that the molecular structure of Chang 7 oil shale kerogen is highly aromatic based on the results of Py-GCMS, solid-state ~(13)C NMR and XPS. For decomposition of Chang 7 oil shale kerogen, the sequence of the bond breakage is determined by the bond order. For cross-linking of Chang 7 oil shale kerogen, H radical is the dominated factor of the formation of thiophene. The number of H2O molecules is more than those of CH4 and CO2 molecules at the same temperature. Compared with HS radicals, HO radicals capture more H radicals to generate H2O molecules. The results are useful for highly efficient development of shale oil in Ordos Basin, China.
查看更多>>摘要：High dimensional stochastic partial differential equations (SPDEs) attract a lot of attention because of their application in uncertainty quantification (UQ) of ore deposits, petroleum geology and other fields. The groundwater flow in heterogeneous media affects all geological processes, including diagenesis, mineralization and oil accumulation. A point of the deep neural network (DNN) method in machine learning is that it can effectively output the solution of the SPDEs with the complex boundaries such as Newman boundary in a direct way. We propose a novel methodology to construct approximate solutions for SPDEs in groundwater flow by combining two deep convolutional residual networks, which can complete the training without interference in the help of an adaptive functional factor. One of the two network models deals with the inner observed points and the other one is required to satisfy the boundary conditions. This model has great potential for solving more complex boundary problems. Another contribution of this work is that the loss function to train the proposed DNN is obtained by deducing the energy functional based on the variational principles, which integrates the physical meaning into the proposed DNN. In addition, we theoretically prove that the minimization loss function problem has a unique solution, and it always conforms to the weak solution form of the problem no matter how the uncertain parameters change. An unconstrained optimization problem, rather than a constrained problem, can be solved without adding any penalty terms. The practical application shows that our model has low computational cost and strong efficiency for solving high-dimensional SPDEs.
查看更多>>摘要：Polymer gels have been widely used to control excessive water production and improve oil recovery. NMR techniques have been gradually applied to analyze the remaining oil distributions in the core during gel treatment in recent years. However, studies about the NMR technique applied to analyze the gelation performance and microscopic water control mechanisms are still immature. In this paper, we first compared the gelation performance of polymer gels using both the Bottle Test method and core displacement method by monitoring the changes in the real-time tested relaxation time (T2) values. Then, the T2 changes of both oil-and water-phase permeabilities after gel treatment in cores were investigated separately. Finally, the disproportionate permeability reduction (DPR) mechanism of polymer gels was investigated by combining the NMR technique in the displacement experiment. Results show that the gelation time of polymer gel in both the tube and the cores can be measured-by the relationship curve between I/T2 and reaction time. The reduction of single-phase permeability of oil after polymer gel treatment is much smaller than that tested by the water phase. T2 data also proves that the polymer gel has certain selective water plugging performance. In addition, when the oil and water coexist, polymer gel's disproportionate permeability reduction (DPR) mechanism is mainly reflected in the gradual increase of oil saturation after gel treatment. However, the flow resistance of water in the pore space gradually increases due to the continuous water absorption and swelling of gels. It can squeeze the crude oil channel during the chase water flooding period. Finally, gel injection in heterogeneous cores can plug the high-permeability layer. MRI results demonstrated that the chase water could divert and flood the low-permeability layer usually with high residual oils, thus improving oil recovery. In summary, this study's results will help further enhance the understanding of polymer gel cross-linking mechanisms in porous media and provide an experimental basis in the analysis of the DPR mechanism of polymer gels. It can also lay a theoretical foundation for the field application of polymer gels.
查看更多>>摘要：Many of western Canada's heavy oil reservoirs are too thin to allow expensive thermal recovery techniques. Therefore, waterflooding is still often employed in heavy oil reservoirs due to its relatively inexpensive and easy manipulation. However, heterogeneity in permeability and/or in water saturation has been widely encountered in heavy oil reservoirs, resulting in very poor oil recovery with excessive water production. In this study, performances of enhanced oil recovery (EOR) by injection of oil-in-water (O/W) emulsions in heterogeneous heavy oil reservoirs were evaluated through experiments, modeling and reservoir simulation. For experiments, each O/W emulsion was firstly characterized via its stability behavior, emulsion quality, oil-water interfacial tension (IFT), oil viscosity, and droplet size distribution. Parallel-sandpack models with two different types of heterogeneities, either in oil saturation or absolute permeability, were applied to simulate the heterogeneity in heavy oil reservoirs. Experimental results show that injection of the emulsions is effective to recover the by-passed oil in the water-unswept sandpack and the EOR performance is strongly dependent on the emulsion characteristics. In order to achieve optimal oil recovery, a conformance in water mobility (outflow percentage ratio of 50%;50%) is found to be effecitve in the parallel-sandpack with heterogeneity in permeability. In parallel-sandpack with variations in water saturation, a strong blockage in high water saturation sandpack with a long penetration distance contributes an optimal oil recovery. An in-house simulator was developed based on an optimized emulsion flow model, for the first time, to be able to describe the physics of emulsion-assisted EOR in heterogeneous porous media. The simulation results show that emulsion injection in parallel-sandpack causes a greater average permeability reduction in the high water mobility sandpack resulted from longer emulsion slug penetration compared to that in the low water mobility sandpack. This leads to an increased water flood rate in the low water mobility sandpack, resulting in more oil production. A case study of emulsion flooding for EOR in a heterogeneous heavy oil reservoir was conducted. Strategies for optimal EOR in heterogeneous heavy oil reservoirs were discussed. Reservoir simulation results show that a greater ratio of emulsion slug in high water mobility zone to that in low water mobility zone is beneficial for EOR with a fixed emulsion plugging strength. For a given emulsion slug, there exists an optimal emulsion plugging strength for a prime EOR which can be designed through the optimized emulsion flow model.
查看更多>>摘要：The shale oil in the Jimsar sag, Junggar Basin, NW China is developed by horizontal wells and volume fracturing, and has the characteristics of a high initial production rate followed by a rapid decline and a low oil recovery factor. Therefore, it is necessary to fnd effective methods for enhancing shale oil recovery rates. The shale formations in the Jimsar sag, Junggar Basin are classifed into three different types;; types I, II and III. Fracturing fuid imbibition, surfactant imbibition and CO2 huff and puff experiments were conducted to evaluate the EOR potential of different types of shale formations. The oil distribution inside the shale core samples with time before, during and after the experiments is monitored using low-feld NMR. The experimental results show that the CO2 huff and puff method had the highest ultimate oil recovery rate, followed by the surfactant imbibition method;; the fracturing liquid imbibition method preformed without the addition of a surfactant had the lowest oil recovery rate. Surfactants improved the oil recovery of spontaneous imbibition by approximately 18% more in the type II and III shale formations compared to that of the fracturing liquid imbibition method. Field applications also demonstrated that adding a surfactant into the fracturing liquid signifcantly improved the oil production in type II and III oil formations relative to the common slick water fracturing liquid without an added surfactant.
查看更多>>摘要：The shale oil in the Jimsar sag, Junggar Basin, NW China is developed by horizontal wells and volume fracturing, and has the characteristics of a high initial production rate followed by a rapid decline and a low oil recovery factor. Therefore, it is necessary to fnd effective methods for enhancing shale oil recovery rates. The shale formations in the Jimsar sag, Junggar Basin are classifed into three different types;; types I, II and III. Fracturing fuid imbibition, surfactant imbibition and CO2 huff and puff experiments were conducted to evaluate the EOR potential of different types of shale formations. The oil distribution inside the shale core samples with time before, during and after the experiments is monitored using low-feld NMR. The experimental results show that the CO2 huff and puff method had the highest ultimate oil recovery rate, followed by the surfactant imbibition method;; the fracturing liquid imbibition method preformed without the addition of a surfactant had the lowest oil recovery rate. Surfactants improved the oil recovery of spontaneous imbibition by approximately 18% more in the type II and III shale formations compared to that of the fracturing liquid imbibition method. Field applications also demonstrated that adding a surfactant into the fracturing liquid signifcantly improved the oil production in type II and III oil formations relative to the common slick water fracturing liquid without an added surfactant.
查看更多>>摘要：Hydraulic fracturing is an effective means for the economic development of shale oil reservoirs. A large volume of water is introduced into the shale matrix during hydraulic fracturing, which leads to the subsequent oil-water two-phase flow in the shale reservoir. The fluid flow behaviors in shale are still ambiguous due to the nonnegligible nanoconfinement effect caused by the omnipresent nanoscale pores and the heterogeneities of mineral types, pore size and wettability of shale. In this study, both the single-phase and oil-water two-phase flow behaviors at pore-scale considering nanoconfinement effects, dual-wettability and pore space characteristic of shale reservoir were investigated using the pore network modeling (PNM) method. We constructed the three-dimension shale digital rock based on scanning electron microscope (SEM) images and extracted the pore network model. The modified single nanotube flow equations considering the adsorption and slip effects were incorporated with the shale pore network model to investigate the single-phase flow at different volumetric total organic content (TOC). Oil-water two-phase flow and relative permeability at different TOC in volume were computed by quasi-static method with consideration of nanoconfinement effect. Results indicated that the TOC and wettability are the primary factors affecting the single-phase flow in shale. Slip effect enhanced the flow capacities of both single-phase water and oil. The wettability effect on two-phase relative permeability curve was not as notable as that on single-phase flow. Oil-water two-phase relative permeability curves were significantly affected by TOC. The two-phase flow region narrows down with the increase of volume TOC, indicating more oil is trapped and thus cannot be displaced. This work provided important insights on pore-scale fluid transport behaviors in shale.
查看更多>>摘要：Machine learning supports prediction and inference in multivariate and complex datasets where observations are spatially related to one another. Frequently, these datasets depict spatial autocorrelation that violates the assumption of identically and independently distributed data. Overlooking this correlation result in over-optimistic models that fail to account for the geographical configuration of data. Furthermore, although different data split methods account for spatial autocorrelation, these methods are inflexible, and the parameter training and hyperparameter tuning of the machine learning model is set with a different prediction difficulty than the planned real-world use of the model. In other words, it is an unfair training-testing process. We present a novel method that considers spatial autocorrelation and planned real-world use of the spatial prediction model to design a fair train-test split. Demonstrations include two examples of the planned real-world use of the model using a realistic multivariate synthetic dataset and the analysis of 148 wells from an undisclosed Equinor play. First, the workflow applies the semivariogram model of the target to compute the simple kriging variance as a proxy of spatial estimation difficulty based on the spatial data configuration. Second, the workflow employs a modified rejection sampling to generate a test set with similar prediction difficulty as the planned real-world use of the model. Third, we compare 100 test sets' realizations to the model's planned real-world use, using probability distributions and two divergence metrics;; the Jensen-Shannon distance and the mean squared error. The analysis ranks the spatial fair train-test split method as the only one to replicate the difficulty (i.e., kriging variance) compared to the validation set approach and spatial cross-validation. Moreover, the proposed method outperforms the validation set approach, yielding a minor mean percentage error when predicting a target feature in an undisclosed Equinor play using a random forest model. The resulting outputs are training and test sets ready for model fit and assessment with any machine learning algorithm. Thus, the proposed workflow offers spatial aware sets ready for predictive machine learning problems with similar estimation difficulty as the planned real-world use of the model and compatible with any spatial data analysis task.
查看更多>>摘要：The shallow marine channels and fans in the Yinggehai Basin have huge hydrocarbon exploration potential. The depositional features and filling processes of the submarine channel and fan system during 10.5-7.2 ma were studied based on seismic data, cores, well curves, and lab data. (1) Five depositional elements were recognized in the channel system, including PSC, MSC, DSC, levee, and abandonment fill, and the deposits show the typical sedimentary structure of debris flow. The filling process of the channel system was divided into three stages based on deposits supply intensity. The stages were made up of several filling units, and each unit possessed at least one complete channelized depositional succession. (2) The fan system was developed under the large-scale "U" shaped feeding canyon, and its filling process was divided into two stages, the shape and distribution were controlled by shelf break and syn-sedimentary faults in the early stage, and was influenced by the localized changes of paleogeomorphology in late stage. The influence factors for the development of gravity-driven systems were summarized into two levels;; regional depositional environment changes and localized distinctive geologic settings. The former one controlled the development of shallow water gravity-driven deposits in the whole basin, mainly including rapid falling of sea-level, moderate subsidence rate, and stable deposits supply. The latter one controlled the morphology and distribution of depositional elements in gravity-driven systems. The preexisting banded low terrain induced by blind faults controlled the shape of the confined channel, and variable faulting intensity in different segments of the syn-sedimentary fault and localized paleogeomorphology changes influenced the distribution of channels and inner fan in the fan system. The study of deposition features, internal architecture, and controlling factors may provide a reference for further exploration in this area and a deeper understanding of gravity-driven processes in shallow marine.
查看更多>>摘要：Well operation optimization is vital in maximizing the net present value (NPV) of the field development plan (FDP). As well operation is a time-series scenario, this problem contains numerous parameters to be optimized and countless solutions. This results in huge optimization time to evaluate each scenario using the reservoir simulator, especially with robust optimization. In this study, particle swarm optimization is coupled with the deep learning-based proxy model to solve such problems in the 2D synthetic and 3D Egg cases. First, the long short-term memory (LSTM) proxy model is developed to estimate the reservoir response to the well operation scenario instead of reservoir simulation. When the drilling and bottomhole pressure (BHP) schedules are entered into the input layer for the proxy model, the average cumulative field oil and water productions and field water injection from the multiple reservoir models are obtained in the output layer. In case of well pattern, the optimized scenario of the location and type of wells in Kim et al. (2021a) was fixed in this study. After the LSTM proxy model fits with the training data pair of time-series BHP schedule and corresponding the responses obtained by ECLIPSE (ECL), it can predict the time-series field response for given BHP scenario. When the calculated NPV from the LSTM is compared with the true NPV from ECL, the coefficient of determination for the validation and test datasets is higher than 0.97, solving the overfitting problem of the simple neural network (NN) proxy model. It is critical to select a proper deep learning algorithm according to the characteristics of the input and output data. While the convolutional NN (CNN) is proper to make a proxy model for well pattern scenario, which consists of the static images with the same dimension in Kim et al. (2021a), the LSTM is suitable for well operation scenario, which changes over time. Second, the drilling and BHP schedules are optimized under the constraint of the maximum available rig. Also, to reduce these high-dimensional optimization variables, we define the BHP schedule as a cosine function instead of the stepwise approach. It reduces the number of training data required for the proxy model. Third, optimization strategies are reviewed for both the well pattern and well operation. The sequential proxy, CNN-LSTM, shows similar NPVs with the results of the sequential ECL-ECL. However, CNN-LSTM requires only 12% of optimization time compared to ECL-ECL for the 3D case. Therefore, CNN-LSTM is proposed as the reasonable solution for reliable decision-making for the FDP in a short time.
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