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Journal of Petroleum Science & Engineering
Elsevier Science B.V.
Journal of Petroleum Science & Engineering

Elsevier Science B.V.

0920-4105

Journal of Petroleum Science & Engineering/Journal Journal of Petroleum Science & Engineering
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    Rock breaking characteristics of cyclic electrohydraulic shockwaves

    Liu, KerouZhang, HuiCai, ZhixiangYu, Qing...
    16页
    查看更多>>摘要：In recent years, to further increase the rate of penetration (ROP), cyclic electrohydraulic shockwaves drilling (EHSD) has been proposed. It is of great significance to study the rock breaking characteristics of this technology. Firstly, a series of experiments were carried out using three different types of sandstone rock samples. The results showed that all the rock samples were fractured after 15 to 40 impacts. Before fracture, the damage of rock samples can be divided into internal damage and external damage, including surface pits, orthogonal central tensile cracks developed from the bottom face, and other tensile cracks. Secondly, based on stress wave theory, a theoretical model for calculating the P-wave peak stress in the rock sample was established, and the calculation was carried out by MATLAB. The simulation results for S2-1 showed that the maximum transmitted compressive stress near the top face reaches 163.5 MPa. The P-wave reflected from the lateral faces formed a superposition area of tensile stress, and the maximum tensile stress reaches 5.44 MPa. With the increase of impact times, the maximum tensile stress gradually decreases and approaches the top face, which explains the formation of the orthogonal central cracks. In addition, it was found that rock samples are subject to the alternating changes of compressive and tensile stress under a single impact. Finally, based on the analysis and discussion of the above results, the rock breaking mechanism of EHSD was analyzed. The results showed that the EHSD mainly damages the rock through the water wedge effect, the tensile stress near the crack boundary, and alternating stress. The research can improve our understanding of how EHSD breaks rocks, which can promote the application of this technology in field drilling.
      原文链接:
    • NSTL
    • Elsevier

    2-D porous flow field reveals different EOR mechanisms between the biopolymer and chemical polymer

    Li, HuaZhu, WeiyaoNiu, HaoGao, Yubao...
    9页
    查看更多>>摘要：Biopolymers are potential flooding agents that can tolerate high temperatures and salinity. The screening of biopolymers has always followed the same rules based on viscosity and elasticity testing as chemical polymers, and the parameters are comparable. However, the current pilot field study of biopolymer flooding did not achieve the expected effect as chemical polymers. Therefore, it was necessary to examine the difference between the two polymer types in the flooding process and examine the EOR mechanisms to explain the suboptimal performance of biopolymers. Traditional microscopic flooding experiments were carried out in the present study, but the results showed no significant difference in recovery of the two polymer types (only 2% original oil-in place (OOIP) difference) or in sweep and the microscopic displacement efficiency, indicating that a traditional experiment is not sufficient to explain the performance difference between the two polymer types. Therefore, to distinguish the two types of recovery processes, micro-PIV experiments were carried out to further quantitatively investigate the flow field characteristics of the two polymers. The results showed that the flow velocity after xanthan gum (XG) injection significantly decreased 88.6% of water injection but only 26.9% after partially hydrolysed polyacrylamide (HPAM), indicating severe pore clogging by XG. Therefore, there are differences in EOR mechanisms: HPAM improves the mobility ratio, while XG clogs pores and changes the flow direction. To understand the reasons for the distinct flow fields, further quantitative analysis was conducted. First, in the margin region with a relatively lower velocity, the effective flow diameter was reduced to 48.04% and 62.69% of the pore width after XG and HPAM injection, respectively, indicating that the adsorption of XG was stronger than that of HPAM, although both could be adsorbed. Second, both velocities after polymer injection remained in a similar low range in wide channels, while the velocity after HPAM increased up to six times higher than XG in narrow channels, indicating that the shear resistance of XG is greater than that of HPAM and is the key mechanism for its higher pore adsorption and pore clogging ability. Therefore, the strong clogging of biopolymers results in poor migration ability in reservoirs, which is the major reason why biopolymers cannot reach deep oil rich regions and show suboptimal recovery. Therefore, understanding how the agents migrate in reservoirs is the key to improving utilization efficiency, and the combination of micro-PIV and pore scale flow experiments is essential for understanding the flow fields and characteristics.
      原文链接:
    • NSTL
    • Elsevier

    Downhole data correction for data-driven rate of penetration prediction modeling

    Encinas, Mauro A.Tunkiel, Andrzej T.Sui, Dan
    13页
    查看更多>>摘要：In recent years, machine learning has been adopted in the Oil and Gas industry as a promising technology for solutions to the most demanding problems like downhole parameters estimations and incidents detection. A big amount of available data makes this technology an attractive option for solving a wide variety of drilling problems, as well as a reliable candidate for performing big-data analysis and interpretation. Nevertheless, this approach may cause, in some cases, that petroleum engineering concepts are disregarded in favor of more data-intensive approaches. This study aims to evaluate the impact of drilling data measurement correction on data-driven model performance. In our study, besides using the standard data processing technologies, like gap filling, outlier removal, noise reduction etc., the physics-based drilling models are also implemented for data quality improvement and data correction in consideration of the measurement physics, rarely mentioned in most of publications. In our case study, recurrent neural networks (RNN) that are able to capture temporal natures of a signal are employed for the rate of penetration (ROP) estimation with an adjustable predictive window. The results show that the RNN model produces the best results when using the drilling data recovered through analytical methods. Moreover, the comprehensive data-driven model evaluation and engineering interpretation are conducted to facilitate better understanding of the data-driven models and their applications.
      原文链接:
    • NSTL
    • Elsevier

    A fractal physics-based data-driven model for water-flooding reservoir (FlowNet-fractal)

    Xu, YunfengHu, YujieRao, XiangZhao, Hui...
    15页
    查看更多>>摘要：This paper proposed a fractal physics-based data-driven framework for reservoir simulation (name as FlowNetfractal) by integrating physics-based data-driven model with fractal theory. FlowNet-fractal enables fast history matching and production prediction of water flooding reservoirs by considering the fractal characteristics of reservoir permeability and porosity. Details of FlowNet-fractal calculation were given with an oil-water twophase flow example. In the FlowNet-fractal, transmissibility, control pore volumes (PVs), fractal mass dimension and fractal index were separately defined in each one-dimensional connection element to map reservoir properties, which more specifically reflected reservoir heterogeneity than the traditional FlowNet method. In this paper, an example of simple heterogeneous reservoir model and two actual water-flooding reservoir cases with different scales were given. The calculation results showed that the FlowNet-fractal method outperformed the FlowNet method in both the convergence speed and the accuracy of history matching. Moreover, the heterogeneity of the reservoir model was also defined by the inversed fractal mass dimension and the fractal index.
      原文链接:
    • NSTL
    • Elsevier

    Spatial and temporal evolution of the Sinian and its implications on petroleum exploration in the Sichuan Basin, China

    Miao, ZhengshuoPei, YangwenSu, NanSheng, Shouzheng...
    13页
    查看更多>>摘要：In recent years, there have been significant discoveries of hydrocarbon resources with commercial value in the Dengying Formation of the central Sichuan Basin. However, the impact of tectonic evolution on hydrocarbon accumulation has not been appropriately documented. In this study, we employed multiple methods, including seismic interpretation, balanced cross-section restoration, deformation quantification, erosion calculation, and paleo-highs/lows reconstruction, to investigate the tectonic kinematics, erosion distribution, paleo-highs/lows evolution of the Sichuan Basin. The quantification of deformation reveals that the Sichuan Basin has experienced a complicated tectonic evolution, with two rounds of transition from tension to contraction since the Sinian. In the Late Ordovician, contraction caused by the Caledonian Orogeny resulted in a foreland basin being developed in the Sichuan Basin. Since the Late Triassic, the Sichuan Basin has experienced intense contraction, resulting in the development of its current structural pattern. By integrating the erosion distribution, the reconstructed paleo-highs/lows maps of Sinian strata indicate the development of inherited paleo-uplifts in the central and southwestern Sichuan Basin from the Early Cambrian to the present. The evolution of paleo-highs/ lows plays a critical control on the hydrocarbon accumulation of the Weiyuan and Anyue gas fields, indicating there could be high potential for hydrocarbon exploration in areas with high slope gradients in the paleo-uplifts.
      原文链接:
    • NSTL
    • Elsevier

    Water sorption and transport in Silurian shales

    Svabova, MartinaVorokhta, Maryna
    8页
    查看更多>>摘要：We have studied the transport of water in shales through gravimetric water vapour sorption experiments. Kinetic data have been evaluated and several kinetic models have been compared, with emphasis on time-dependent diffusivity models. The effective diffusivities as a function of relative pressure show an initial increase corresponding to the sorption to primary adsorption centres, followed by water cluster formation. The final decrease in effective diffusivity is connected with flattening of the pore-mouth meniscus. Introducing the flow rate kinetic parameter enables samples with different amounts of adsorbed water to be compared, showing that the highest value of the flow rate kinetic parameter is connected with the sample with largest surface area and the greatest micropore volume.
      原文链接:
    • NSTL
    • Elsevier

    Porosity prediction from pre-stack seismic data via committee machine with optimized parameters

    Gholami, AminAmirpour, MasoudAnsari, Hamid RezaSeyedali, Seyed Mohsen...
    14页
    查看更多>>摘要：Prediction of porosity from the seismic data via geophysical methods when limited number of wells are available is a challenging task that has high uncertainties. This study aims to construct a hybrid data-driven predictive model to establish a quantitative correlation between seismic pre-stack (SPS) data and the porosity. First, three intelligent models that are optimized by bat-inspired algorithm (BA): optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR) are constructed for relating porosity to the SPS data. Then, to benefit from all individual optimized models, a final hybrid model was built via committee machine (CM) where single models are combined with a proper weight to predict porosity in the reservoir space. This approach is examined on the SPS data from an oil field in the Persian Gulf with a single exploratory well where input parameters (Vp, Vs, and rho) to the AI models are derived from a two-parameter inversion method. We found that the coefficient of determination, root mean square error, average absolute relative error, and symmetric mean absolute percentage error for the CM are 0.923615, 0.015793, 0.132280, and 0.061310, respectively. Moreover, based on four statistical indexes that are calculated for each model, CM outperformed its individual elements followed by the OSRV. A comprehensive analysis of the results confirms that CM with the OM elements is a superior approach for computing porosity from the SPS in the well and then throughout the entire reservoir volume. This strategy can aid petroleum engineers to have a better forecast of porosity population in the reservoir static model immediately following the data that is obtained from the first exploratory well. Ultimately, successful implementation of this approach will promptly delineate sweet spots that can replace uncertain and complicated conventional geophysical methods.
      原文链接:
    • NSTL
    • Elsevier

    Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models

    Brenjkar, EhsanDelijani, Ebrahim Biniaz
    22页
    查看更多>>摘要：Rate of penetration (ROP) prediction, can assist precise planning of drilling operations and can reduce drilling costs. However, easy estimation of this key factor by traditional or experimental models is very difficult. This requires comparing available models for achieving the best prediction approach. In this study, four machine learning (ML) methods and two traditional ROP models were utilized to predict ROP. ML techniques include multilayer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). MLPNN, ANFIS, and RBFNN methods were trained with four meta-heuristic algorithms including particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and genetic algorithm (GA). The backpropagation (BP) algorithm was also incorporated to train the ANFIS and MLPNN methods as a conventional method. For comparison purposes, the traditional ROP models of Bourgoyne and Young (BYM) and Bingham were also implemented in combination with four meta-heuristic algorithms. The required data were collected from the mud logging unit (MLU) and the final report of a drilled well located in southwestern of Iran. In the MLU, the information of different sensors is firstly collected through the data communications protocols and sent to a master unit to be processed into relevant dimensions (i.e. create operational variables). In order to accurately record information, the sensors are calibrated at regular intervals. After removing the outliers, the overall noise of data was reduced by SavitzkyGolay (SG) smoothing filter. Then, in order to simulate a drilling process in a realistic manner and also to evaluate the performance of the models in approximating the penetration rate at greater depth of hole, the available data were divided into 6 sections based on depth. After that, sections 2 to 6 were separately used as a test dataset and all previous sections were considered as a training dataset. Results indicated that PSO-MLPNN achieved the highest performance in comparing with other developed models. The concluding remark is that ML models are more efficient and reliable than traditional models. In addition, combining ML models with metaheuristic algorithms can achieve better results than conventional algorithms such as BP. The results of this study can be used as a practical guide for the management and planning of future well drilling.
      原文链接:
    • NSTL
    • Elsevier

    Petrophysical rock typing based on deep learning network and hierarchical clustering for volcanic reservoirs

    Wang, WeifangWang, ZhizhangLeung, Juliana Y.Kong, Chuixian...
    14页
    查看更多>>摘要：With the rebound of international oil prices, complex non-clastic volcanic rock reservoirs have been gaining much attention from the scientific community in recent years. However, due to the complexity of volcanic diagenesis and the intensity of tectonic activity at its development location, volcanic reservoirs generally exhibit strong heterogeneity. In particular, the volcanic rock reservoir in Block Jinlong 2 is a low-medium porosity, ultra low to low permeability reservoir with complex lithology and diverse storage spaces. Therefore, the author proposes a petrophysical rock typing method based on the VGG16 deep learning network and hierarchical clustering, which takes MCP curve images and physical data as input. First, the author uses the learning ability of the convolutional layer and max-pooling layer in the VGG16 network to extract the characteristics of each MICP curve. Second, through the PCA dimensionality reduction method, the feature of each picture is transformed into a low-dimensional feature vector. Then, the distance of the image feature vector and the distance of the physical point are combined into a new distance matrix, which is used as the input of hierarchical clustering. Finally, the scores of the Davies Boulding Index and Sillhouette Coefficient are used to determine the final clustering result. Comparing with the FZI and FZI* methods, this method has a better application effect in Jinlong-2 Block volcanic reservoirs. The volcanic rocks of Jinlong 2 are divided into 4 types. Type 1 has the best reservoir characteristics, type 4 is relatively compact, and type 2 and 3 are in the transition zone, but type 2 will develop some micro fractures to increase reservoir seepage capacity. Such petrophysical rock typing has laid a good foundation for the subsequent reservoir description and reservoir evaluation research of Jinlong-2 Block volcanic reservoirs.
      原文链接:
    • NSTL
    • Elsevier

    Numerical study of hydraulic fracture propagation in inherently laminated rocks accounting for bedding plane properties

    Zhang, YulongLiu, ZaobaoHan, BeiZhu, Shu...
    16页
    查看更多>>摘要：The objective of this paper is to investigate hydraulic fracture propagation in inherently laminated rocks considering different bedding plane characteristics. To this end, a layered particle-based numerical model is first established in the framework of particle flow simulation. The mechanical behavior of rock matrix is controlled by randomly distributed bond contacts while that of bedding planes by preferentially orientated smooth joint contacts. On this basis, an improved hydromechanical coupled model is then proposed by modelling of hydraulic pipes according to contact types, which can well describe the fluid flow difference of rock matrix and bedding planes. The efficiency of improved model is assessed by comparisons with the Blanton's criterion and typical experimental evidences. Numerical predictions are in good agreement with analytical solutions. The interaction modes between induced fractures and bedding planes are also captured successfully. Hydraulic fracturing simulations of laminated rocks are then conducted and quantitatively analyzed in terms of borehole pressure and fracture propagation. Some key parameters such as the elastic, strength, permeability and thickness of bedding planes effect on hydraulic fracturing process are further investigated and discussed.
      原文链接:
    • NSTL
    • Elsevier