查看更多>>摘要： One of the existing tight oil reservoir development methods is development after hydraulic fracturing of horizontal wells. This type of development method severely limits the recovery rate of tight oil reservoirs. The other conventional enhanced oil recovery methods are not suitable for tight oil reservoirs. The water injection performance of fractured vertical wells is poor, and water breakthrough easily occurs during water injection in horizontal wells. In order to improve the recovery factor of tight oil reservoirs and to achieve sustainable development of tight oil reservoirs, finding a suitable method of enhancing the recovery factor of right oil reservoirs is the top priority. Based on the concept of periodic asynchronous water flooding to supplement energy between horizontal fractures, in this paper, an effective method of improving the recovery rate of tight oil reservoirs is discussed. Physical simulation experiments were designed to improve the recovery efficiency of horizontal wells with an inter-fracture asynchronous injection-production cycle (IFAIPC). The feasibility of the method was verified. Two different sets of experiments were designed: 1) the experiment that simulates a homogeneous reservoir with a section with two fractures (M1), and 2) an experiment that simulates a heterogeneous reservoir with three sections of four fractures (M2) and the fourth section with five fractures (M3). The results show that during the IFAIPC process, the permeability of the rock sample tends to decrease gradually. In particular, in the first five cycles, the permeability decreases significantly. Water injection becomes difficult as the permeability decreases, and there is a feasible critical value for water injection. In homogeneous reservoirs, water breakthrough is slow. The degree of recovery and the ultimate recovery rate before water breakthrough are high. The use of IFAIPC in heterogeneous reservoirs can effectively increase the swept volume and delay the rise of water saturation. The IFAIPC method can effectively improve the recovery of tight oil reservoirs by more than 10% in the first five injection-production cycles. The experiments confirm the feasibility and effectiveness of using the IFAIPC method in tight oil reservoirs.
查看更多>>摘要： Nuclear magnetic resonance (NMR) measurement provides lithology-independent porosity and pore structure for sedimentary reservoirs. Recently, oil and gas were discovered in igneous rock formations, however, NMR logging porosity is substantially lower than the true porosity, and the T_ distributions does not characterize the pore-size distributions. These observations limit the use of NMR logging in igneous rock formations and new research is needed to be developed. To address this gap in the literature, using numerical simulation of NMR behavior, we conduct a theoretical study to examine the impact of magnetic susceptibility, echo spacing, and fluid type on the T2 distributions in igneous rock. The simulation results and laboratory experiment show that, the paramagnetic minerals affect the magnetization of fluids in pores and rate of the echo decay, and NMR porosity is thus significantly reduced. To effectively correct the NMR measurement, a new approach is proposed to improve the underestimation of NMR porosity and T2 distribution shifting to shorter times. For a pore model, the corrected porosity and T2 distribution are both in good agreement with the model. In a case study from Chepaizi area of Junggar Basin, China, the corrected NMR porosity matches well with core measurement, and the corrected T2 distribution shifts to longer times more than before. Moreover, bound fluid volume and movable fluid volume are both corrected, and the NMR permeability also agrees well with core laboratory measurement, which proves that the correction method effective. Therefore, this technology enables NMR log for evaluating the igneous rock reservoir quality.
查看更多>>摘要： The effective permeability of a laminated structure is an important parameter related to the practical necessity of coarsening of grid blocks in large scale reservoir simulations. Traditional effective permeability derived under steady state flow conditions can cause great errors in the transient flow process due to the great heterogeneity and anisotropy of laminated formations. To handle this error, the unsteady oil flow in the matrix between hydraulic fractures of a laminated system during an oil depletion process is analyzed in this study. Based on material balance, we present an analytical method for calculating the dynamic effective permeability of the laminated structure considering the cross flow between laminae in the transient flow process. The proposed dynamic effective permeability can be used to conduct coarsening of grid blocks and can be incorporated into numerical models for accurate modeling of transient flow in a laminated structure in the reservoir simulation. It is shown that the simulation results using the proposed effective permeability model match well with those from two-dimensional (2D) simulations. Further, the effects of the horizontal permeability, vertical permeability, thickness of the laminae, and the length of the matrix between fractures are studied.
查看更多>>摘要： Polymer gels are potential plug agents to solve near wellbore issues. To meet the requirements of harsh reservoir conditions, enhancing the thermal stability and strength of polymer gels is still a challenging problem. In this study, we discuss the properties of phenolic/aldehyde-based polymer gels, aiming to provide suggestions of gel formulations for temporary blocking in SAGD wells. A power law relation, with an exponent of 1.2, was found between the modulus of HP AM gels and the polymer concentrations. It is also found that the strength of gels was increased with the hydrolysis degree and molecular weight of HP AM. Acetic acid and silica nanoparticles were proved to be effective additives to enhance the thermal stability of gels. It is demonstrated that acetic acid catalyzes the decomposing of hexamethylenetetramine (HMT) at low temperatures, while protects the dihy-droxybenzene (DBH) from oxidation at higher temperatures. Experiments results showed that the HPAM gels could be stable for more than 7 days at 175 °C, while the co-polymer with heat resistance groups improved the applied temperature to 200 °C. Rupture pressure measurements showed that the polymer gels exhibit great blocking efficiency in sandpacks. However, the rupture pressure in wellbore is relatively lower as a result of squeeze out, rather than the destruction of gel structure. Finally, filed applications in 4 vertical wells and 1 horizontal well were introduced in detail. It is demonstrated that the gel plugs are applicable in a wide range of wellbore temperatures, from 60 °C to 191 °C.
查看更多>>摘要： The accurate reconstruction of three-dimensional(3D) structure of porous media is crucial for predicting their physical or mechanical properties. Reconstructing the 3D structure from its reference two-dimensional(2D) image is an intractable inverse problem. Although many traditional methods have been proposed to address this problem, their low efficiency and poor performance limit their applicadon. Recently, the deep learning(DL) based methods have attracted widespread attention. However, the existing DL-based methods suffer from some serious defects, including heavy demand for training samples, unstable training and high GPU memory requirement. In this study, a recurrent neural network(RNN) based model, namely 3D-PMRNN, has been proposed to overcome these weaknesses. To the best of our knowledge, it is the first time that RNN model has been applied to solve the 2D-TO-3D reconstruction problem. Benefiting from this innovative architecture, the model just requires one 3D training sample at least and reconstructs the 3D structure layer-by-layer. Furthermore, the proposed model can reconstruct a larger-scale realization(2563 or larger) due to the novel network architecture, less demand for GPU memory and stable training process. The reconstruction efficiency has also been greatly improved compared to the traditional methods. Three experiments are carried out on isotropic and anisotropic porous media to verify the model's performance on accuracy, diversity and generalization. Experimental results indicate that the synthetic realizations have good agreement with the testing target in terms of visual observation and quantitative comparison.
查看更多>>摘要： N2 or N2/CO2 mixture is considered as injection gas to enhance oil recovery from Tahe oil field. The reservoir pressure and temperature are around 66 MPa and 414 K. The target light crude oil is produced from S117 well of the Tahe oil field. The two main objectives of this study are: 1) to find an effective fluid characterization procedure to calculate the MMP for the N2/CO2-S117 oil system accurately. 2) to identify the effect of injection gas composition (i.e. N2/CO2 mixture) on the gas drive mechanisms for the S117 light crude oil. The PVT and multiple-contact experimental data were measured and these data were used to characterize and validate different fluid characterization methods, including the 'conventional methods' and the direct PnA method. We proposed an integrated fluid characterization procedure to accurately calculate MMP. The novelty of the proposed fluid characterization procedure can be summarized as: 1) the procedure regains excellent match in both phase envelopes and critical points before and after lumping; 2) an improved direct PnA regression procedure with only two perturbation parameters was used to calculate pseudo-component properties. The comparison of calculated MMP to the experimental slim-tube data taken from literature shows that the integrated procedure using the direct PnA characterization has the best performance on MMP calculation. For the CO2/N2-S117 system, the vaporizing-gas drive mechanism controls the miscibility provided the CO2 mole fraction in the injection gas is less than 70%. The calculated MMP is independent of the CO2 mole fraction within the range approximately from 0 to 70% in the injection gas. At a higher CO2 mole fraction (ranging from 71% to 100%), the complex condensing/vaporizing (C/V)-gas drive mechanism dominates the miscibility and thus the MMP of the fluid system decreases dramatically as CO2 mole fraction increases.
查看更多>>摘要： The two-phase flow and heat transfer of a Cu-Al2O3 water hybrid nanofluid in a wavy enclosure partially filled with a porous medium is investigated. The concentration gradient of the composite nanoparticles is modeled considering the thermophoresis and Brownian motion nanoscale forces. The porous medium is also modeled using the local thermal non-equilibrium model. The governing equations are converted into a non-dimensional form and then solved using the finite element technique. The impact of the Darcy number, convection interface, and the wave amplitude on the concentration distribution of nanoparticle flow and heat transfer is addressed. The outcomes show that the convective heat transfer in the liquid and solid phases could be increased by 4.5 and 2.7 folds by increasing the Darcy number from 10~(-5) to 10~(-2). The growth of the concentration of the nanoparticles from 0 to 0.04 improves the liquid Nusselt number by 17%. The hybrid nanofluid shows a better heat transfer enhancement compared to simple nanofluids.
查看更多>>摘要： Porosity prediction and analysis is crucial for reservoir delineation, characterization and well placement. Basically, porosity measurements are obtained from well logs and core samples. However, methods based on core samples and well logs are sometimes challenging, time-consuming and very expensive. In this paper we attempt to predict and analyze porosity using machine learning algorithms. Porosity prediction is performed as a supervised multiple regression problem. Every data point of the training sample from the study area is made of lithology and acoustic impedance which are defined as independent variables or predictors while measured value of porosity is the dependent variable, provided as the label to be predicted. Deep neural network, Random forest and Decision tree algorithms were subjected to training and learning of rich and proper features that are important for the prediction of porosity thus, good generalization ability is crucial to the successful training of a machine learning model. Therefore, we used several optimization functions to train the deep neural network in order to choose the best performing one, on which we retrained the model using k-fold cross-validation technique. Although all the algorithms showed very good performance, deep neural network proved to be the most efficient with 0.042% of the mean squared error as the learning loss and 0.051% of the training mean absolute error. Our result was further tested for effectiveness by using new set of non-labeled lithology and acoustic impedance data and the result was compared with measured porosity and the result showed 89% of the Pearson correlation. Compared with measured porosity, prediction results displayed accurate prediction of porosity within milliseconds thus saving the working time. The result of this study shows that machine-learning algorithms can be time saving and reliable alternative for reservoir porosity prediction and analysis.
查看更多>>摘要： The vertical resolution of seismic data is important for the interpretation of geologic features at a fine scale. To improve the resolution of seismic data, spectral enhancement has progressed mainly due to one-dimensional (1D) convolutional models and deconvolution filters; however, recent studies have applied machine learning (ML) algorithms. To generate successful outcome using ML techniques, it is important to reflect the features of target data in the training stage of the ML model. One of main features of seismic field data is a non-stationary wavelet over time due to the change of frequency content as the wavelet passes through specific regions, such as a gas reservoir. However, since conventional methods are generally based on a 1D convolutional model, which assumes that the propagating wavelet is stationary, they show low performance for the parts where the frequency content of the wavelet is significantly changed. In this study, we developed a spectral enhancement algorithm using ML techniques, which can be applied to a seismic trace with different frequency contents over time. We showed that sparse spike inversion results of all target data, rather than just well log data, can be useful to obtain information on the reflectivity series for generating a training data set. To verify the performance of the new algorithm, we compared the spectrally enhanced results from the ML model trained by stationary wavelets and time-variant wavelets and confirmed that the latter wavelets provided better results. In addition, we proposed a quality control (QC) method for verifying the spectrally enhanced results.
查看更多>>摘要： Building machine learning (ML) models based on pressure-volume-temperature (PVT) data is of paramount importance to capture trends and predict fluid behavior in a very heterogeneous and highly nonlinear thermodynamic system. PVT samples stored in an oil company database are often not complete and might be missing properties; both black oil and compositional. Before delving into building optimized fluid models, it is required to have a clean and structured PVT database complete with all the required properties. We present multiple novel algorithms developed to accurately predict a complete set of black oil and compositional properties within a PVT database. The proposed methodology consists of predicting properties in series, starting from a minimal set of data (black oil and compositional) and obtaining a complete set of data for all PVT samples. The order through which this is completed relies on benefiting from the existing data to predict missing data starting from the highest correlating data to the lowest. We also honored the physical nature of correlations between properties, and consequently, ranked properties for prediction as this leads to less error propagation. In addition, we have implemented data clustering prior to training ML models. Clustering is used to categorize the fluid samples into families based on the collective behavior of their different features, and hence, improve the quality of the PVT samples' properties prediction using machine learning. Several options are tested where clustering is performed using black oil properties only, compositional properties, or a combined set of black oil and compositional properties and the clustering scenario leading to the least prediction error is adopted. We have trained ML models to generically predict all black oil and compositional properties resulting from laboratory experiments, including molecular weights (MW) and mole fractions of heavy fractions (any set of heavy fractions) from the mole fractions of all the commonly available components up to C7+ and molecular weight of C7+. The massive data set used in this paper enabled comprehensive testing of the developed algorithms and provided striking accuracy of the predicted PVT properties; especially the compositional ones. Despite all the significant efforts shown in the literature concerning predicting PVT properties, the missing link is a systematic methodology to complete PVT samples' properties in a consistent manner. Furthermore, the focus in the literature is mainly on forecasting black oil properties; compositional properties are scarcely considered. Algorithms developed in this paper address these two limitations and are tested using a uniquely large data set available for onshore and offshore fields and reservoirs in Abu Dhabi. No previous algorithms, to the best of our knowledge, are tested on such a large data set.
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