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Based on the microscopic pore-throat characterization of typical continental tight reservoirs in China, such as sandstone of Cretaceous Qingshankou and Quantou formations in Songliao Basin, NE China sandy conglomerate of Baikouquan Formation in Mahu area and hybrid rock of Lucaogou Formation in Jimusaer sag of Junggar Basin, NE China the theoretical lower limit, oil accumulation lower limit, effective flow lower limit and the upper limit of tight oil reservoirs were defined by water film thickness method, oil bearing occurrence method, oil testing productivity method and mechanical balance method, respectively. Cluster analysis method was used to compare the differences in pore-throat structure of different tight reservoirs, determine the grading criterion of tight reservoirs, and analyze its correlation with the limit of reservoir formation. The results show that the boundary between tight reservoir and conventional reservoir corresponds to the upper limit of physical properties, the boundary of class II and class III tight reservoirs corresponds to the lower limit of effective flow, the boundary of class III and class IV tight reservoirs corresponds to the lower limit of reservoir forming, and the theoretical lower limit of tight reservoir corresponds to the boundary between tight reservoir and non-reservoir. Finally, the application results of the grading evaluation criterion show that the tight oil productivity is highly controlled by the type of tight reservoir, and class I and class II tight reservoirs are the favorable sections for high production of tight oil.

A gold tube simulation device was used to study the cleavage of representative compounds into gas. The goal of this study is to investigate hydrogen isotope composition change of gaseous hydrocarbons during maturity. Gas chromatography and isotopic analyses were conducted to determine how the yield of natural gas components and their hydrogen isotopic composition were related to experimental temperature and heating rate. A chemical kinetic model for the generation of each component of the natural gas and for the hydrogen isotopic fractionation was established and calibrated based on the results. Results indicate that the hydrogen isotopic fractionation during the evolution of various gas-forming organic materials can be satisfactorily described by chemical kinetic models. During regular methane generation, the reactions at low–activation–energy region had a greater contribution than the high–activation-energy region. While the reactions with high–activation-energy region had greater contribution of deuterium-rich methane. Compared with carbon isotope fractionation, this results in a greater hydrogen isotopic fractionation, which is more sensitive to changes in maturity. This study lays a foundation for further investigations of genesis and maturity of natural gas provided by hydrogen isotopic fractionation. It also provides fundamental knowledge for investigating the filling history of natural gas reservoir and for identifying.

To evaluate the gas content characteristics of nanopores developed in a normal pressure shale gas reservoir, the Py1 well in southeast Chongqing was selected as a case study. A series of experiments was performed to analyze the total organic carbon content, porosity and gas content using core material samples of the Longmaxi Shale from the Py1 well. The results show that the adsorbed gas and free gas content in the nanopores developed in the Py1 well in the normal pressure shale gas reservoir range from 0.46–2.24 m3/t and 0.27–0.83 m3/t, with average values of 1.38 m3/t and 0.50 m3/t, respectively. The adsorbed gas is dominant in the shale gas reservoir, accounting for 53.05–88.23% of the total gas with an average value of 71.43%. The Gas Research Institute (GRI) porosity and adsorbed gas content increase with increasing total organic carbon content. The adsorbed gas and free gas contents both increase with increasing porosity value, and the rate of increase in the adsorbed gas content with porosity is larger than that of free gas. Compared with the other five shale reservoirs in America, the Lower Silurian Longmaxi Shale in the Py1 well developed nanopores but without overpressure, which is not favorable for shale gas enrichment.

Currently, the existing techniques of evaluating quality of teaching quality colleges and universities within China are based on classical and statistical methods. However, these methods are unable to accurately reflect the real and actual evaluation of teaching quality. Furthermore, in this era of computerization, education has also revamped itself and is not limited to the conventional lecturing approach. Nowadays, English has become one of the most important skills for foreigners as countries move toward internationalization resulting in a huge amount of data being collected in educational databases, which remains unused. Powerful tools are required to improve the quality of English teaching and reap the benefits of big data generated in classrooms. In this paper, online analytical processing (OLAP) in combination with a support vector machine (SVM) classification algorithm is adopted, and then the algorithm constructs the linear optimal decision function in the feature space. Through the training of sample data by the SVM algorithm, relatively high-quality classification results can be obtained on the target object, especially for high-dimensional cases. The proposed approach has an extremely efficient application value. Compared with the existing methods, the error of evaluation results can be greatly improved, which leads to further improvement of accuracy in terms of evaluation results.

To improve the precision of predicting sweet spots in tight oil reservoirs, the classification of tight oil reservoir storage space is necessary. The tight oil reservoir in the lower submember of the first member of Shahejie Formation (Es1L) of the Raoyang Sag of the Jizhong Depression in the Bohai Bay Basin was selected as a case study for testing a new method of classifying tight oil reservoir storage space based on the results of porosity, permeability, and mercury intrusion porosimetry measurements. The pore‐throat size ranges were classified according to pore‐throat fractal characteristics and were divided into three groups: 1000 nm. The oil‐bearing property of tight oil reservoir samples is closely related to the median and maximum pore‐throat radii. The inflection and intersection points in the trend lines of the pore volume percentages connected by different pore‐throat widths (1000 nm) vs the porosity and permeability values (which represent the relationship between the pore volume connectivity characteristics and porosity/permeability) were used as the basis for classifying the tight oil reservoir storage space. The tight oil reservoir storage space in the Es1L formation of the Raoyang Sag was divided into three categories, namely classes of III, II, and I. The porosity ranges of the three tight reservoir storage space classes are

Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

To investigate the densities and thicknesses of adsorbed methane in a shale gas reservoir, the Lower Silurian Longmaxi Shale in Southeast Chongqing was selected as a case study to establish models of the pore size distributions contributed by various components (PSDCVC), the amounts of methane adsorbed by various components (AMAVC) and the density and thickness of methane adsorbed by various components (DTMAVC). Based on the results of total organic carbon, carbon content in organic matter (OM), X-ray diffraction, methane isothermal adsorption and low-temperature nitrogen adsorption measurements, the pore size distributions and the amount of methane adsorbed by OM, clay and other minerals were calculated using the PSDCVC and AMAVC models. The average densities and thicknesses of adsorbed methane in pores with widths of

To evaluate the effects of the composition, laminar structure and burial depth on pore volumes connected by pore-throats of different sizes in a shale oil reservoir, the Es1x formation in the Raoyang Sag of the Bohai Bay Basin was selected as an example. The effects of component contents, laminar structure and burial depth on pore volumes connected by pore-throats with widths of

To quantitatively evaluate the pore size distributions (PSD) contributed by various components (PSDCVC) in over-mature shale samples, the Upper Ordovician Wufeng Shale from Southeast Chongqing was taken as an example. The PSDCVC model was established using the experimental results including total organic carbon (TOC), carbon element content in organic matter (OM), X-ray diffraction mineral composition analysis and low-pressure N2 adsorption/desorption (LPNA) tests, which were performed on three sets of subsamples. The PSDs associated with OM, clay, other components and shale sample per unit mass were obtained, and the PSDs of the subsamples calculated from PSDCVC method were compared with the results of LPNA and field emission scanning electron microscopy methods to confirm the credibility of PSDCVC results. For the shale samples from the same layer, the pore volumes (PV) of different pore widths associated with OM, clay and other components per unit mass are corresponding constants or similar to these constants, respectively. The PSDs associated with OM, clay and other components are primarily distributed in the pore widths of 0.22% is mainly contributed by OM.

The splicing of backscattered scanning electron microscopy (SB-SEM) method was applied to evaluate the microscopic pore characteristics of the Lower Silurian Longmaxi Shale samples from Py1 well in Southeast Chongqing, China. The results from SB-SEM, including frequencies, volumes and specific surface areas of organic and inorganic pores with different sizes, were compared with those of low temperature nitrogen adsorption/desorption (LTNA) and mercury intrusion porosimetry (MIP). The results show that the changes in organic and inorganic surface porosity with increasing image area estimated from the SB-SEM method become almost stable when the SB-SEM image areas are larger than 0.4 mm2, which indicates that the heterogeneities of organic and inorganic pore volumes in shale samples can be largely overcome. This method is suitable for evaluating the microscopic pore characteristics of shale samples. Although the SB-SEM underestimates the frequencies, volumes and specific surface areas of pores smaller than its resolution, it can obtain these characteristics of pores larger than 100 nm in width, which are not effectively evaluated by the LTNA method and are underestimated by the MIP method.

To evaluate the amount of adsorbed methane in a shale reservoir under different conditions of temperature and pressure, the Longmaxi shale from the Py1 well of southeast Chongqing was selected as a case to establish the three compositions adsorbing methane (TCAM) method based on the results of total organic carbon, X-ray diffraction, and methane isothermal adsorption analyses. The amounts of methane adsorbed by organic matter (OM), clay, and other minerals were calculated under different conditions of temperature and pressure, followed by the evaluation of the occurrence characteristics of adsorbed gas in a shale reservoir. A comparison of data from experiments and the TCAM method with two shale samples revealed the uncertainty of TCAM. The results from the two samples range from 0.02 to 0.08 m3/t and from 0.01 to 0.09 m3/t, with average values of 0.05 and 0.04 m3/t, respectively. The methane adsorption capacities of OM, clay, and other minerals at temperatures ranging from 30 to 70 °C were 26.04–38.76, 1...

Based on the thermal simulation experiments of shale and crude oil samples, the amount of gas generated from kerogen and oil cracking during each geological period was calculated based on the chemical kinetic method. The results showed that the total amount of gas generated from the Niutitang Shale in Hy1 well is 49.00 m3/t, including gas generated from kerogen (17.76 m3/t) and gas generated from oil cracking (31.24 m3/t). The period of gas generated from kerogen is from the Cambrian to the Carboniferous with amount of gas generated value of 14.77 m3/t, which is mainly from the Lower Ordovician to the Middle Ordovician (510–470 Ma). The period of gas generated from oil cracking is from the Ordovician to the Triassic, and the main period of gas generated from oil cracking is from the Lower Devonian to the Upper Permian (410–250 Ma). The amounts of gas generated from oil cracking are 13.30 m3/t, 9.44 m3/t, and 3.77 m3/t in the Devonian, Carboniferous, and Permian, respectively.

Taking the southern Songliao Basin as the target area, the hydrocarbon expulsion volume from source rocks has been quantitatively evaluated in this paper, based on the material balance method. Employing the “Overpressure” module of PetroMod software, the overpressure history of the source rocks was evaluated. According to the relationships between hydrocarbon expulsion rate, residual organic carbon content and overpressure within the source rocks, the lower limits of expulsion from tight oil source rocks were determined: a hydrocarbon expulsion amount of 2 mg/g, residual hydrocarbon content of 0.8%, and overpressure of 1 MPa. The source rocks with hydrocarbon expulsion amounts > 8 mg/g, a residual organic carbon content > 2.0%, and overpressure > 7 MPa were defined as excellent source rocks. As a result, tight oil source rocks can be divided into three types, excellent source rocks (type I), inefficient source rocks (type II) and non-productive source rocks (type III). The actual application in the southern Songliao Basin shows that the excellent source rocks have an obvious control on the planar and vertical distribution of tight oil discoveries, areas with excellent source rocks and nearby formations are favorable for the accumulation of tight oil. Key words: tight oil, source rocks, grading evaluation, hydrocarbon expulsion, overpressure, southern Songliao Basin

A new method for classifying tight oil reservoir storage space was introduced. The pore-throat size ranges are clssified according to pore-throat fractal characteristics, and then the tight oil reservoir storage space is classified according to the relationships between the reservoir quality and the pore volume percentage connected by pore throats with different fractal characteristics.

Measuring total porosity in shale gas reservoir samples remains a challenge because of the fine-grained texture, low porosity, ultra-low permeability, and high content of organic matter (OM) and clay mineral. The composition content porosimetry method, which is a new method for the evaluation of the porosity of shale samples, was used in this study to measure the total porosity of shale gas reservoir samples from the Lower Silurian Longmaxi Formation in Southeast Chongqing, China, based on the bulk and grain density values. The results from the composition content porosimetry method were compared with those of the Gas Research Institute method. The results showed that the composition content porosimetry porosity values of shale gas reservoir samples range between 2.05% and 5.87% with an average value of 4.04%. The composition content porosimetry porosity generally increases with increasing OM and clay content, and decreases with increasing quartz and feldspar content. The composition content porosimetry results are similar to the gas research institute results, and the differences between the two methods range from 0.05% to 1.52% with an average value of 0.85%.

Nano-scale pores play an important role in determining the adsorption capacity of shale gas. To quantitatively evaluate the pore size distributions contributed by organic matter (OM), clay and other minerals in an over-mature shale reservoir, the Lower Silurian Longmaxi shale from Southeast Chongqing was selected as a case study and was analyzed by the pore size distributions contributed by OM, clay and other minerals (PSDCOCO) method. Based on the results of total organic carbon (TOC), carbon element content in OM, X-ray diffraction (XRD) and low temperature nitrogen adsorption/desorption (LTNA) experiments, the pore size distributions contributed by a unit mass of OM, clay and other minerals were calculated and the differences between the results of the LTNA and PSDCOCO methods were determined for the four verified samples. The average value of the difference between the experimental and calculated pore volumes is 0.154 × 10−3 cm3/g. For the Longmaxi shale from the Py1 well, the contributions of OM and other minerals to the pore volume (pore width

Qiannan Depression was selected as a study case to confirm the contribution of organic pores (microscale and nanoscale pores within organic grains in shale) formed by hydrocarbon generation to shale gas reservoir space. Using the material balance principle combined with chemical kinetics methods, an evaluation model of organic porosity for shale gas reservoirs was established. The results indicate that there are four important model parameters to consider when evaluating organic porosity in shale: the original organic carbon (w(TOC0)), the original hydrogen index (IH0), the transformation ratio of generated hydrocarbon (F(Ro)) and the organic pore compression coefficient (C). The organic porosity of carbonaceous shale in the middle and lower part of the Niutitang Formation in the Cy1 well is from 0.68 to 5.24%, and the average value is 3.01%.

To compare experimental methods for describing shale pore features, we performed a series of parallel experiments on typical continental shales from the Bohai Bay Basin of eastern China, including scanning electronic microscopy (SEM), focused ion beam scanning/field emission scanning electron microscopy (FIB/FE-SEM), low-temperature nitrogen adsorption/desorption (LTNA), mercury intrusion porosimetry (MIP) and nuclear magnetic resonance (NMR) techniques. The results show that artificial peaks located at 3 to 5 nm, affected by the tensile strength effect (TSE), are clearly seen in the BJH pore size distributions (PSDs) calculated using the low-temperature nitrogen desorption curve of shale with narrow-neck pores (e.g., ink-bottle pores). Although the LTNA technique cannot completely reveal large pores, the adsorption/desorption curve and the PSD acquired from it can still indicate the relatively high or low extent of development of large pores in different shales. Shale within the “oil window” primarily contains inorganic pores, as organic nanopores are generally formed during the high or over-mature stage of organic matter. During the mercury intrusion, high pressures forcing liquid mercury to enter small pores (less than 40 to 70 nm) will cause pores in the samples to crack, especially for shale with slit-shaped and wedge-shaped pores. Unlike LTNA, MIP measures only pore throats and does not detect pore bodies; therefore, the effective pore size revealed by the LTNA method may be up to 300 nm instead of only 50 nm when LTNA is combined with MIP to reveal the PSD of shales with narrow-necked pores. NMR and LTAN can be complemented by each other, and the PSD results revealed by them are anastomotic, which suggests that the combination of NMR and LTAN is an ideal method for revealing the PSD of shale.

Marine sourced clay minerals and organic matter enrichment as clay-organic nanocomposites within shale has attracted attention because of their changeable surface areas, nanoporosity, and methane adsorbability. Here marine shale samples were used to investigate the characteristics of clay-organic nanocomposites and their impact on gas adsorption response. The results show that organic nanolayers mainly occur as nanometer intercalations with clay nanolayers. High-resolution scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images provide direct evidence of the parallel aligned clay-organic nanocomposites with an approximate thickness of each organic and clay nanolayer and the positive correlations between their morphology, occurrence, and relative content. However, such clay-organic interactions are significantly damaged by structural deformation, accompanied by a weakening in gas adsorption capacity. We suggest that the naturally occurring structures that hydrocarbon molecules are attached or resided in fine-grained marine shales are predominantly associated with clay-organic nanocomposites, which play an important role as adsorption carriers. Moreover, the clay-organic association may be an effective mechanism of gas adsorption. These observations and investigations could challenge the common perception that discrete kerogen and clay minerals are the major components for gas adsorption.

The Lower Silurian Longmaxi Shale in Southeast Chongqing of Sichuan Basin in China is considered to be a potential shale gas reservoir by many scholars in recent years. The special shale gas well, namely, Pengye-1 well, was selected as a case study to evaluate the characteristics of the shale gas reservoir. A series of experiments were performed to analyze the geochemical, mineralogical, and petrophysical features and gas content using samples of the Longmaxi Shale from Pengye-1 well. The results show that the organic and inorganic porosities of these samples are range of 0.08-2.73% and 0.06-2.65%, with the average of 1.10% and 1.76%, respectively. The inorganic pores primarily contribute to the porosity until the TOC content is more than 3%. Organic matter plays an important role in adsorbed gas content. The adsorbed gas is dominant in the Longmaxi Shale of Pengye-1 well, which ranges from 0.46 to 2.24 cm3/g, with an average of 1.38 cm3/g. The free gas content ranges from 0.45 to 0.84 cm3/g with an average of 0.68 cm3/g, and is 24.4-49.7 percent of total gas with an average of 37.5%. The bottom part of the Longmaxi Shale is the most favorable for shale gas exploring, which is higher of brittleness mineral content, porosity and gas content. Compare with the other five shales in America, the Lower Silurian Longmaxi Shale is derived from older sedimentary periods with significantly higher thermal maturity and has experienced several periods of intense tectonic, which are unfavorable for the shale gas enrichment.

This article performed a series of parallel experiments with numerical modeling to reveal key factors affecting the gas adsorption capacity of shale, including shale quality, gas composition and geological conditions. Adsorption experiments for shales with similar OM types and maturities indicate that the OM is the core carrier for natural gas in shale, while the clay mineral has limited effect. The N 2 and CO 2 adsorption results indicate pores less than 3 nm in diameter are the major contributors to the specific surface area for shale, accounting for 80% of the total. In addition, micropores less than 2 nm in diameter are generated in large numbers during the thermal evolution of organic matter, which substantially increases the specific surface area and adsorption capacity. Competitive adsorption experiments prove that shale absorbs more CO 2 than CH 4 , which implies that injection CO 2 could enhance the CH 4 recovery, and further research into N 2 adsorption competitiveness is needed. The Langmuir model simulations indicate the shale gas adsorption occurs via monolayers. Geologically applying the adsorption potential model indicates that the adsorption capacity of shale initially increases before decreasing with increasing depth due to the combined temperature and pressure, which differs from the changing storage capacity pattern for free gases that gradually increase with increasing depth at a constant porosity. These two tendencies cause a mutual conversion between absorbed and free gas that favors shale gas preservation. During the thermal evolution of organic matter, hydrophilic NSO functional groups gradually degrade, reduce the shale humidity and increase the gas adsorption capacity. The shale quality, gas composition and geological conditions all affect the adsorption capacity. Of these factors, the clay minerals and humidity are less important and easily overshadowed by the other factors, such as organic matter abundance.

In modern buildings, increasing the probability of an electrical fault. Therefore, in the electrical fault detection, the application of fault neural network have being more and more widely. This paper presents a diagnostic method based on using Probabilistic Neural Networks (PNN) to classify forecasting. The key is training and calculating data to classify fault types, which is collected from ten different position in fault circuit. Diagnostic results show that The diagnosis results show that the PNN algorithm can have a very good diagnostic effect. The correct diagnosis rate is 90%.