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This paper is aimed at introducing a method for the division of development units of thick carbonate reservoirs. This method consists of eight steps, ranked according to impact weight, each of which is independent but intrinsically linked. When there is a conflict between studies in different steps, the results of the previous step take precedence. (1) Pressure is the most important and reliable data. When the wells with an abnormal pressure gradient in the same interval account for more than 60%, further division of the reservoir is appropriate. (2) Baffles are the second most important and reliable basis. When there are continuous baffles or poor reservoirs that encounter more than 80% wells, it is appropriate to further divide reservoirs. (3) Without the two mentioned above, but with an unconformity surface or discontinuity surface between two sequences, it is appropriate to treat these two sequences as two development units. (4) Without the three mentioned above, if the permeability above and below the sequence boundary differs by 10 or more times, reservoirs above and below the sequence boundary are appropriate as distinct development units. (5) When the thickness, continuity, distribution pattern, and microstructure of two adjacent sequences are significantly different, it is appropriate to treat these two sequences as two development units. (6) If the development units are based on sequences, check for homogeneity within the stratigraphy. If depositional facies or physical property discontinuities are present, the sequence would be reconsidered for subdivision. (7) The reserves in each development unit should account for more than 20% of that of the whole reservoir. (8) When the division of the development units is complete, it needs to be examined to ensure that there is a consistent structure, fluid property, and free water level. If inconsistencies exist, then subdivision is considered. The method was successfully applied to A oilfield, Central Arabian Basin, in combination with the cores, cast thin sections, regular or special core analysis, wireline logging data from 450 wells, formation pressure from 63 wells, and more than 100 wells with a production logging test. The Mishrif reservoir was divided into four development units, in which different well patterns and well types were deployed. The improved development effect proves that the division of development units for thick bioclastic limestone reservoir is reasonable. [ABSTRACT FROM AUTHOR]

Rock grain size parameter is the key parameter of reservoir rock physics analysis. The study found that the relationship between the NMR T 2 spectrum and rock grain size distribution curve is directly related to NMR T 2 distribution and grain size distribution of rock, so you can use T 2 to retrieve the size distribution of rock NMR spectral data. Based on the calculation of the rock core experiment results of grain size distribution by NMR T 2 distribution using the conversion method of piecewise nonlinear calibration for each core, adjusting the calibration parameters to make the grain size distribution calculated with the core analysis of grain size distribution approximation, the error is minimum to obtain each core scale parameter conversion. In the end, the core parameters are classified according to the parameters of pore and permeability. Finally, according to the actual NMR T 2 distribution curve inversion of underground rock granularity, the inversion results contrast with the core analysis results, and the reliability of the method is verified to provide accurate, continuous rock size distribution profile analysis of geological reservoir rock physics. [ABSTRACT FROM AUTHOR]

In order to clarify the gas-water two-phase flow law in horizontal wells and study the gas-water two-phase flow characteristics in horizontal wells, firstly, the gas-water two-phase in a horizontal well is numerically simulated and analyzed, and the flow pattern distribution under different well inclination angles and different phase separation flow rates is obtained. Secondly, a series of production logging instruments including CAT instrument was used to conduct experimental research on gas-water two-phase flow under different flow conditions, and the measured values of each CAT probe were extracted to reflect the local holdup under different flow patterns. Finally, SSA-BP neural network algorithm is used to identify a gas-water two-phase flow pattern in a wellbore by using experimental parameters such as center holdup, well inclination angle, spinner revolution, and CAT probe measurements. The recognition accuracy of the neural network was improved from 83.75% to 91.66%, and the operation speed was accelerated. It provides a research idea to explore the flow characteristics of gas-water two-phase flow in horizontal wells. [ABSTRACT FROM AUTHOR]

As an important stratum of exploration in the Tarim Basin, which is the largest petroleum basin in China, Yingshan Formation has great potential. The relationship between the fine division of microfacies and reservoir space characteristics has not been systematically studied which restricts further exploration. On the basis of the analysis of core, thin section, logging data, imaging logging data (FMI), and field outcrop, the microfacies types and reservoir space characteristics of the Ordovician Yingshan Formation are systemically studied. The results show that (1) nine microfacies (Mf1-Mf9) and four microfacies associations (MA1-MA4) were identified within the Ordovician Yingshan Formation. MA1-MA2 represent a medium- to high-energy sedimentary background, and MA3-MA4 represent a medium- to low-energy sedimentary background. (2) Four reservoir space types were observed in the Yingshan Formation, which are dominated by fracture-dissolution pore type, followed by small dissolution vug type, seldom fracture type, and large-scale karst-cavity type. (3) High-energy microfacies are the material basis of favorable reservoirs. The high-energy microfacies associations are prone to small dissolution vug and fracture-dissolution pore types of reservoirs; large cave reservoirs can sometimes be developed in the high part of the sequence and form favorable reservoirs in the Yingshan Formation. The low-energy microfacies associations mostly appear as original strata or interlayer, but after the superposition of structural cracks, buried karsts, or epigenetic karsts, low-energy microfacies associations may form relatively small-scale fractured and fracture-dissolution pore types of reservoirs in this area. [ABSTRACT FROM AUTHOR]

Over the past few years, significant exploration breakthroughs have been made in the sandy conglomerates reservoir of the Triassic Baikouquan Formation in the Mahu Sag, Junggar Basin, China. However, the elasticity and pore characteristics of the tight sandy conglomerates reservoir in this area are still unclear. Through a systematic sonic testing program of the sandy conglomerates sample collected from the Mahu Sag, the regularity behind the variation of the elasticity and pore characteristics of the sample has been analyzed and captured. The Triassic Baikouquan Formation of the slope of Mahu Sag has been through complex burial and diagenetic history. The compaction, cementation, and dissolution are the three types of diagenesis that have the greatest impact on pore characteristics in the study area. In order to quantitatively describe the influence of this complex burial and diagenetic history, we present a rock-physics modeling approach to describe the elastic properties of sandy conglomerate sample. The overall strategy of the modeling is to combine the contact cement theory (CCT), differential effective medium (DEM) and the upper Hashin-Shtrikman bound. The CCT model can well describe the history of cementation, DEM model is suitable for the history of dissolution, and HS upper bound is used to describe the history of compaction. Then, the model is used to describe well log data from sandy conglomerates reservoirs in Mahu Sag and successfully explain the rock porosity, pore shape trends, and the elasticity in the sandy conglomerate reservoirs. [ABSTRACT FROM AUTHOR]

Accurate evaluation of coalbed methane (CBM) content plays a momentous role in the identification and efficient development of favorable exploitation blocks of CBM resources, but there are still many technical challenges in the exploration and development of onshore CBM fields. With the development and application of geophysical logging technology, using geophysical logging data to predict the gas content of CBM reservoirs has been proven to be an effective and feasible solution. However, the complex logging response of the CBM reservoirs makes it difficult to characterize the relationship between the gas content and the logging curve response by a simple linear relationship. In this paper, kernel extreme learning machine (KELM), a machine learning method, is combined with the geophysical logging data to predict the vertical variation curve of gas content in CBM wells. In this paper, the laboratory data on coal rock gas content from 12 CBM wells in the Southern Shizhuang block are selected, and a CBM content prediction model based on the KELM method is constructed by selecting the log curves, combining cross-validation and grid-seeking to determine the hyperparameters, and validating the prediction model using the test dataset and a new well in the same block. The application of the model on the test dataset was remarkable, and the vertical variation of CBM content obtained by applying it to the new well was consistent with the laboratory results, which proved the correctness and generalizability of the model. The results of this paper show that the CBM content evaluation model based on the KELM method and geophysical logging data is applicable to the 3# coal seam in the target block and can be used to predict the vertical CBM content of CBM wells; compared with the extreme learning machine (ELM) method and the backpropagation neural network (BPNN) method, the KELM method requires fewer hyperparameters to be explored when constructing the CBM content evaluation model, and the model construction is simple and has high prediction accuracy. At the same time, the CBM content model constructed by the KELM method differs for different blocks, coal seams at different depths, and different response ranges of geophysical logging data. The construction of a CBM content prediction model using the KELM method and logging curves is an effective means of characterizing CBM resources, and the model construction process and evaluation criteria studied in this paper can be used to help other blocks evaluate the CBM content, providing guidance for further exploration and development of CBM fields with practical application. [ABSTRACT FROM AUTHOR]

Long12 block of Longhupao oilfield is located in the western slope area of Qijia-Gulong Sag, which lay in the central depression of Songliao basin. Recent exploration and development shows that Putaohua oil layer has great potential storage. However, the block is located in the ring concave fault zone, with complex geological background and extensive low resistivity oil layers and water layers of high resistivity, resulting in an unclear condition of reservoir forming and oil-water distribution rules of Putaohua oil layer, which deeply restricts the pace of increasing storage and production of the oilfield. In this study, the data of logging, logging, drilling, analysis, and testing are adopted to study carries out a reservoir forming conditions such as source rocks, reservoirs, caprocks, traps, and migration conditions. Meanwhile this study discuss the main controlling factors of Putaohua reservoir formation in combination with the law of oil-water distribution, so as to provide a geological basis for clarifying the reservoir forming law of long 12 block and the prediction of favorable target areas. [ABSTRACT FROM AUTHOR]

It is difficult to predict tight sandstone gas in continental rift basin because of complex deep structure, fast transformation of sedimentary facies, large lateral variation of reservoir thickness, and tight and strong heterogeneity. Based on the petrophysical analysis of logging data, this paper improved the traditional low-frequency establishment method of prestack synchronous inversion which is under the constraints of fine isochronal sequence stratigraphy framework. The prestack AVO has attributed as covariate, and Bayes kriging method has applied to establish low-frequency model. Then, the relationship between density and P-wave and S-wave impedance has been introduced. The results show that the stability of density parameters has been improved, and the facies controlled prestack inversion with drilling verification has been realized which the number of layers with thickness error less than 20% accounts for 75.2%. Exploration practice shows that the facies controlled prestack inversion technology has important reference significance for other deep continental fault basins. [ABSTRACT FROM AUTHOR]

The majority of the Jurassic Yan'an Formation reservoirs in the Jingbian Oilfield of the Ordos Basin have entered the middle and late stages of development, and fine characterization of reservoir heterogeneity and recognition of the remaining oil distribution are the keys to formulating comprehensive adjustment for production. The Y92-1 layer of the Yan'an Formation Y9 reservoir in the LZ area of Jingbian Oilfield is taken as the research object in this paper. Based on logging data, core, and other basic data, the reservoir morphology in the study area is finely characterized. Single-channel units are subdivided, and the reservoir configurations of different hierarchical levels are analyzed qualitatively and quantitatively. Finally, combined with the plane profile consistency analysis of the morphological characteristics of the sand body in layer Y92-1 and the injection production mode, it is concluded that there may be four distribution patterns of the remaining oil in the study area, and the composite river channel sand body dominated by the lateral superimposed mode under the inverse lateral accretion shale bed tendency injection condition is a favorable enrichment area for the remaining oil. This mode is mainly distributed in the boundary part of the different periods of single sand body superposition. These understandings not only deepen the significance of tapping the potential of the remaining oil in frequently rerouted meandering rivers but also could provide effective guidance for the further remaining oil tapping. [ABSTRACT FROM AUTHOR]

Nanpu 1-29 Area of Jidong Oilfield in China is currently in the stage of high water cut and low recovery degree. The remaining oil development and adjustment are difficult because of the complex reservoir heterogeneity and the lack of analysis of the reservoir architecture due to the unclear river type. This study first used sedimentary background, granularity, core, and sand body distribution to determine the river type and believed it belongs to the wandering braided river. Combined with core and logging data, four types of sedimentary architectural elements were found in the study area, namely, channel bar, braided channel, floodplain, and basalt. Based on the scale measurement of many similar modern wandering braided rivers, core data, and empirical formulas, the scales of the braided rivers were determined. The quantitative relationships among the scale of the braided river, the channel bar, and the braided channel were established. With this constraint, the reservoir architecture was anatomized. The results showed two filling types of the braided channels, including sandy filling and muddy filling. The combination patterns of the channel bars and the braided channels could be divided into three types, namely, superimposed, standalone, and contact, and the contact type was the primary type. On the whole, it showed the geometric morphological characteristics of the flat top convex at the bottom of the channel bar and the flat bottom convex at the top of the braided river channel. A careful measure of the architectural element was executed. The length of the single braided flow belts was 365.16-1349.72 m, and the width was 270.57-1160.54 m. The channel bar's length was distributed 158.89-318.32 m, and the width was distributed 75.97-116.41 m. The braided river's width was distributed 16.81-180.05 m. The length and width ratio of the channel bar was concentrated between 2 and 4, which manifested wide bar and narrow river channel mode. Finally, the static distribution model and dynamic response curve were compared to verify the correctness of the reservoir architecture characteristics to guide the subsequent development of the oilfield. [ABSTRACT FROM AUTHOR]

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone. [ABSTRACT FROM AUTHOR]

Effective permeability is a key parameter for evaluating reservoirs and their productivity. With the wide application of resistivity logging tools in drilling, the advantages of resistivity logging in response to the dynamic invasion process of drilling fluids have become increasingly prominent. We analyzed the variation law of the measured resistivity data for different permeability formations at different times. In this study, we proposed an effective permeability modeling method based on the time-lapse resistivity logging data. First, based on the resistivity measurement data at different times, the dynamic resistivity profile of the reservoir was obtained through joint inversion; we then obtained the invasion depth and invasion zone resistivity of the drilling fluid at different times, along with the original formation resistivity. Subsequently, combined with parameters such as the soaking time, fluid viscosity, and saturation change of the drilling fluid, we obtained the phase permeability curve of the reservoir and dynamic effective permeability of the fluid near the wellbore. This study provides basic parameters for subsequent formation analyses and productivity prediction and substantially improves the reservoir evaluation technology from static to dynamic. [ABSTRACT FROM AUTHOR]

A new method for the determination of oil and water flow rates in vertical upward oil-water two-phase pipe flows has been proposed. This method consists of an application of machine learning techniques on the probability density function (PDF) and the power spectral density (PSD) of the power spectrum output of an ultrasonic Doppler sensor in the pipe. The power spectrum characteristic parameters of the two-phase flow are first determined by the probability density function (PDF) method. Then, the transducer signal is preprocessed by distance correlation analysis (DCA), and independent features are extracted by principal component analysis (PCA). The extracted features are used as input to a least-squares fit, which gave the oil flow rates as output. In the same way, the transducer signal is also preprocessed by partial correlation analysis (PCA), and independent features were extracted using independent component analysis (ICA). The extracted features were used as inputs to multilayer back-propagation neural networks, which water cuts as output. The present method was used to calibrate an ultrasonic Doppler sensor to estimate the flow rates of both phases in oil–water flow in a vertical pipe of diameter 159 mm. Predictions of the present method were in good agreement with direct flow rate measurements. Compared to previously used methods of feature extraction from the ultrasonic Doppler power spectrum signals, the present method provides a theoretical basis for the interpretation of ultrasonic multiphase flow logging data. Ultrasonic multiphase flow logging has potential application value in the production profile logging and interpretation evaluation of production wells with low fluid production and high water cut. [ABSTRACT FROM AUTHOR]

Due to poor physical properties and strong heterogeneity of Daniudi tight gasfield (China), traditional methods are not ideal for predicting reservoir permeability. Based on geoscience data mining algorithms and modeling techniques, this parameter is predicted and characterized from a new perspective. The high precision hydraulic unit information was outputted by the BP neural network and the interwell database was predicted by sequential indicator simulation based on the logging and core data, the exponential and power law relationship functions were selected to participate in the construction of the permeability model. The results showed that the facies-controlled modeling system for permeability based on the hydrodynamic characteristics of tight gas reservoirs could effectively improve the accuracy of reservoir prediction, and the logging information in the longitudinal direction and the facies information in the plane were combined by the hydraulic unit. [ABSTRACT FROM AUTHOR]

The total organic carbon (TOC) content is a critical parameter for estimating shale oil resources. However, common TOC prediction methods rely on empirical formulas, and their applicability varies widely from region to region. In this study, a novel data-driven Bayesian optimization extreme gradient boosting (XGBoost) model was proposed to predict the TOC content using wireline log data. The lacustrine shale in the Damintun Sag, Bohai Bay Basin, China, was used as a case study. Firstly, correlation analysis was used to analyze the relationship between the well logs and the core-measured TOC data. Based on the degree of correlation, six logging curves reflecting TOC content were selected to construct training dataset for machine learning. Then, the performance of the XGBoost model was tested using K -fold cross-validation, and the hyperparameters of the model were determined using a Bayesian optimization method to improve the search efficiency and reduce the uncertainty caused by the rule of thumb. Next, through the analysis of prediction errors, the coefficient of determination ( R 2 ) of the TOC content predicted by the XGBoost model and the core-measured TOC content reached 0.9135. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 0.63, 0.77, and 12.55%, respectively. In addition, five commonly used methods, namely, Δ log R method, random forest, support vector machine, K -nearest neighbors, and multiple linear regression, were used to predict the TOC content to confirm that the XGBoost model has higher prediction accuracy and better robustness. Finally, the proposed approach was applied to predict the TOC curves of 20 exploration wells in the Damintun Sag. We obtained quantitative contour maps of the TOC content of this block for the first time. The results of this study facilitate the rapid detection of the sweet spots of the lacustrine shale oil. [ABSTRACT FROM AUTHOR]

The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Δ Log R method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development. [ABSTRACT FROM AUTHOR]

By taking the third member of the Dongying Formation in the Cha71 fault block of the Chaheji oilfield as an example, the single sand body of the deltaic front underwater distributary channel is meticulously depicted by using the data of well logging and performance production. Portrays the vertical separation model, total lateral separation type, vertical type, lateral superposition type, 4 types of single sand body vertical superimposed and bay type, bank contact between docking, instead of four kinds of single sand body lateral contact type, and summarizes its logging facies identification. The quantitative prediction model of the single sand body was established, the characteristics of single sand body plane distribution were summarized, and the identification of the oil-water layer and the lower limit of reservoir effective thickness were studied. And we got the conclusion that based on the fine characterization of the single sand body and the lower limit standard of effective reservoir thickness, the distribution range of the effective reservoir and connecting unit is determined. Finally, the connectivity of the connecting unit is verified by dynamic data. [ABSTRACT FROM AUTHOR]

With the further exploration and development of shale oil and gas, cycle division of fine-grained sedimentary rock strata has been increasingly highly focused on by scholars. Owing to the application of the theory of classical sequence stratigraphy based on water depth changes and its technical methods being unsatisfactory in the isochronous division and correlation of deep-water fine-grained sedimentary strata, the cycle division of fine-grained sedimentary rock strata has always been a difficult point in the study of sequence stratigraphy. In this paper, the Milankovitch cycle recorded from the study interval and the environment response characteristics were studied, with the lacustrine shale in the lower third submember of the Paleogene Shahejie Formation (lower Es3 submember) in Well Fanye-1 of the Dongying sag, Bohai Bay Basin, as the object of study, by such technical means as thin section identification and X-ray whole rock diffraction, based on such data as logging data and geochemistry, combining the methods of spectral analysis, wavelet transform, and modulus extremum. The results showed that the stratigraphic cycle thicknesses caused by long eccentricity, short eccentricity, and obliquity periods were 38.95 m, 12.98 m, and 4.10 m, respectively, and a total of 16 short eccentricity periods and 4.5 long eccentricity periods were identified in the study interval. Thus, it was further calculated that the sedimentation time was approximately 1.905 Ma, and the average sedimentation rate was estimated to be 0.105 m/ka. Studies have shown that the sedimentary environment of lacustrine fine-grained sedimentary rocks is controlled by the astronomical period, based on which the climate as a whole changes from relatively dry and cold to warm and wet when the eccentricity increases. The identification of the Milankovitch cycle of the lacustrine fine-grained sedimentary strata will provide references for the study of high frequency sequence and the division of high-resolution sequence strata, which can effectively solve the scientifically difficult isochronous division and correlation of lacustrine shale strata. [ABSTRACT FROM AUTHOR]

Based on the physical quantity of log data, the accurate identification of oil- and gas-bearing properties may be caused by the prestack inversion of fluid prediction, which will affect the success rate of exploration and development. Prestack data contain more information of amplitude and frequency. Using the frequency-dependent viscoelastic impedance equation and Bayesian inversion framework, the objective function of frequency-dependent elastic impedance inversion can be established to realize the frequency-dependent impedance inversion at different angles. According to the elastic impedance equation of the frequency-varying viscoelastic fluid factor, the relationship between elastic impedance and the frequency-dependent viscoelastic fluid factor is established, and the prestack seismic inversion method of the frequency-dependent viscoelastic fluid factor is studied. However, one of the important factors easily neglected is that we have been using logging data to establish fluid-sensitive parameters and the lithophysical version for fluid identification, so there are differences between logging and seismic frequency bands for fluid identification. The indicator factors with higher sensitivity to fluid can be selected by laboratory measurements. This article applies this method on Luojia oilfield data and verifies this method with log interpretation results, based on the sample of rock physics obtained in a low-frequency rock physics experiment; the technique of dispersion and fluid-sensitive parameters is studied, and the fluid prediction technology of a multifrequency band rock physics template is adopted, which can build the relationship between rock physical elastic parameters and fluid properties by the multifrequency broadband impedance method. [ABSTRACT FROM AUTHOR]

The architecture analysis of the different orders sedimentary bodies is of great significance to the efficient development of oil and gas fields. In order to investigate the effects of the architectural interfaces on reservoir quality and heterogeneity, this study takes the Gudong oil field as a case to investigate the architectural characteristics of hierarchical bounding surfaces using detailed descriptions of core and wireline logs. Architectural models from the 7th-order to the 3rd-order are analyzed, and the developmental characteristics of the 5th-order braided river, 4th-order single sandstone, and 3rd-order accretion are summarized. The interlayer between two braided rivers is floodplain mud deposition, with poor physical properties, stable thickness, and strong blocking capacity. Two models of interlayers are found in the 4th-order deposition. The first interlayer is between the braided filling channel and midchannel bar, which is composed of generally fine-grained sediments with calcium cementation and poor physical properties. The second interlayer is a transformation belt between two midchannel bars and is generally composed of gravel-scoured deposition with penetration capability. The 3rd-order surfaces are defined as the surfaces of accretions within midchannel bars. Two models of interlayers are also found in the 3rd-order surfaces of accretions. The paleocurrent of the sandy braided river is reconstructed by synthesizing the core data, well logging data, and production performance data. A total of 1 fluvial system (7th-order), 2 compound braided rivers (6th-order), 11 braided rivers (5th-order), 41 midchannel bars (4th-order), and 96 accretions (3rd-order) are developed in the study area. [ABSTRACT FROM AUTHOR]

The Achimov Formation is one of the most important oil- and gas-bearing strata in the West Siberian basin in Russia. The total estimated reserves of this stratum exceed one billion tons. The formation was first explored in 1981, but it remains largely underdeveloped due to its deep burial depth and poor physical properties. Therefore, further research on the genetic mechanisms and distribution characteristics of the reservoirs in the formation can contribute to its further exploitation. The Achimov Formation is dominated by of fine- to medium-grained sandstones interbedded with shale. Based on analysis of well logging data, hand specimens, and previous research, this study analyzed the properties of three members (Ach1, Ach2, and Ach3) of the Achimov Formation and summarized their distribution patterns. Research on reservoir rocks from different oil and gas fields reveals varying physical properties across the formation with permeability and porosity increasing from the northern to central areas and decreasing from the central to the southern areas. Burial depth is one of the major controlling factors for reservoir properties in the formation. Reservoirs in both the northern and southern parts of the formation are buried deeper than those in the central areas, resulting in a disparity in reservoir quality. [ABSTRACT FROM AUTHOR]

Due to the diversity of pore types, it is challenging to characterize the Middle East's Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method and r apex can characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability, r apex , flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery. [ABSTRACT FROM AUTHOR]

The resistivity index is an important parameter for determining the rock saturation index. However, the saturation index changes greatly in unconventional reservoirs, which leads to oil saturation estimation with great difficulty. Hence, we try to establish the relationship between the resistivity index and log data. Firstly, a novel model of estimating the resistivity index with T 2 time was derived based on fractal theory, the relationship between nuclear magnetic resonance (NMR) T 2 spectrum and capillary pressure curve ( T 2 - P c ), and Archie formula. It regards the logarithm of the resistivity index as the dependent variable, with T 2 time and T 2 time when water saturation is 100% as the independent variables. Second, 17 cores were drilled, and T 2 spectrum and the relationship between the resistivity index and water saturation ( I r - S w ) were jointly measured. Next, the experimental results were substituted into the established model to get the model parameters via the multivariate statistics regression method. Then, the experimental data engaged and not engaged in modeling were used to test the established model. The average relative errors of estimated resistivity indices and experimental results are smaller than 8%, and those of the regressed saturation index are smaller than 5%. Finally, the established model was applied in log data processing and interpretation with good effects. It thus proves that the method of the estimating resistivity index with T 2 time is reliable, which provides a novel solution for determining rock electrical parameter of unconventional reservoirs. [ABSTRACT FROM AUTHOR]

The Wu 2 section of the Ke017 well block is a low-resistance gas reservoir with ultralow porosity and low permeability. The comprehensive analysis of rock lithology, physical properties, sedimentary characteristics, and gas content demonstrated that the development of micropores in illite/smectite dominated clay minerals together with the resulted additional conductivity capability and complex reservoir pore structures, as well as the enrichment of self-generating conductivity minerals like zeolites and pyrite which were the formation mechanisms of low-resistance gas layers in the Wu 2 section. A low-resistance gas reservoir has poor physical property, and it is difficult to distinguish the oil layer from the dry, gas, or water layers. In this paper, based on well/mud logging data and laboratory data, by taking advantages of the "excavation effect" of neutron gas and the dual-lateral resistivity difference between different depths, we successfully established a set of low-contrast log response methods for the identification and evaluation of oil layer and formation fluids. For a gas layer, the difference between neutron porosity and acoustic (or density) porosity is smaller than 0 and the difference in dual-lateral resistivity is greater than 0. For a water layer, the neutron porosity is similar to the acoustic (or density) porosity and the dual-lateral resistivity difference will be less than 0. While for a dry layer or a layer with both gas and water, the difference in porosity as well as dual-lateral resistivity is very small. The proposed method effectively solves the technical problem of oil layer and formation fluid identification in low-resistance gas reservoirs. [ABSTRACT FROM AUTHOR]

The Besa River Formation mainly comprises argillaceous sediments, and the upper shale bed of the Formation has been considered as a prospective shale gas reservoir. The maturity and quantity of organic matter found in source rock are considered as factors for evaluating resource value. Original hydrogen index (HIo) and total organic carbon (TOCo) are considered as properties to estimate generated gas volume in shale gas reservoirs. Generally, TOCo and HIo are determined via geochemical analysis of organic matter and calculated from well-log data like density, sonic data, and resistivity. In this study, these properties were measured or calculated by using geological analysis data and bulk-density log, and then the prospective area from the geochemical perspective in Liard Basin is defined by realizing a 3-dimensional static geochemical model. To validate the static model, the gas-in-place (GIP) (496 Bcf/section) of the production well in this study area was compared with GIP (262 Bcf/section) in the static model. Expulsion efficiency was considered as 0.6. The result implies that this model has a method of informative assessment with regard to undeveloped shale gas resources. The static model provides spatial information for generated and retained gas volume in the Besa River Formation, Liard Basin. [ABSTRACT FROM AUTHOR]

The rock physics experiments and fracture toughness tests of shales from the Lower Silurian Longmaxi Formation in the Sichuan Basin in China were carried out. Based on this, the calculation model of the fracture toughness was constructed, thus, the single well evaluation of the fracture toughness in shale formation would be obtained based on the well logging data, which can be used to summarize the spatial distribution characteristics of the fracture toughness in the shale formation. However, it is difficult to obtain transverse distribution characteristics of fracture toughness in shale formation based solely on the well logging data. Therefore, in order to investigate the spatial distribution of the fracture toughness, jointing well logging and seismic method could be adopted to quantitatively predict the fracture toughness in shale formation. The results show that fracture toughness of shales is sensitive to acoustic interval transit time and wave impedance. The prediction model of the fracture toughness of shales was constructed, which had a good prediction effect. The fracture toughness values of shales from the Upper Silurian Wufeng-Longmaxi Formation were larger, whereas those of shales from the Lower Silurian Wufeng-Longmaxi Formation were lower. The fracture toughness is mainly distributed in strips along the vertical direction while the distribution area is continuous in the lateral direction, indicating that it has obvious stratification characteristics. [ABSTRACT FROM AUTHOR]

Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value. [ABSTRACT FROM AUTHOR]

Stimulated reservoir volume (SRV) which is generated by horizontal drilling with multistage hydraulic fracturing governs the production in the shale gas reservoirs. Although microseismic data has been used to estimate the SRV, it is high-priced and sometimes overestimated. Additionally, the effect of stress sensitivity on SRV is not considered in abnormal overpressure areas. Thus, the objective of this work is to characterize subsurface fracture networks with stress sensitivity of permeability through the shale gas well production data of the early flowback stage. The flowback regions are first identified with the flowback data of two shale gas wells in South China. Then, we measured the permeability stress sensitivity of the core after fracturing, coupled to the dynamic relative permeability (DRP) calculation to obtain an accurate and simple DRP curve. After that, a comprehensive model is built considering dynamic two-phase relative permeability function and stress sensitivity. Finally, we compared the calculated results with the microseismic data. The results show that the proposed model could reasonably predict the SRV using the flowback data after fracturing. Additionally, compared with the microseismic data, the stress sensitivity should be included, especially in the abnormal overpressure block. It is believed that this mathematical model is accurate and useful. The work provides an efficient approach to estimate stimulated reservoir volume in the shale gas reservoirs. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods. [ABSTRACT FROM AUTHOR]

Natural gas hydrate is considered as one of the best potential alternative resource to address the world's energy demand. The available geological data at the Mallik site of Canada indicates the vertical heterogeneities of hydrate reservoir petrophysical properties. According to the logging data and sample analysis results at the Mallik 2L-38 well, a 2D model of geologically descriptive hydrate-bearing sediments was established to investigate the multiphase flow behaviors in hydrate reservoir induced by gas recovery and the effects of perforation interval on gas production performance. Firstly, the constructed model with vertical heterogeneous structures of permeability, porosity, and hydrate saturation was validated by matching the measured data in the Mallik 2007 test. The excessive residual methane in the hydrate reservoir observed in simulated results indicates insufficient gas production efficiency. For more effective methane recovery from a hydrate reservoir, the effect of perforation interval on long-term gas production performance was investigated based on the validated reservoir model. The simulation results suggest that both the location and length of the perforation interval have significant impact on hydrate dissociation behavior, while the gas production performance is mainly affected by the length of the perforation interval. To be specific, an excellent gas release performance is found in situations where the perforation interval is set at the interface between a hydrate reservoir and an underlying water-saturated zone. By increasing the perforation interval lengths of 5 m, 8 m, and 10 m, the gas release volumes from hydrate dissociation and gas production volumes from production wells are increased by 34%, 52%, and 57% and 37%, 58%, and 62%, respectively. [ABSTRACT FROM AUTHOR]

This study proposes a deep neural network- (DNN-) based prediction model for creating synthetic log. Unlike previous studies, it focuses on building a reliable prediction model based on two criteria: fit-for-purpose of a target field (the Golden field in Alberta) and compliance with domain knowledge. First, in the target field, the density log has advantages over the sonic log for porosity analysis because of the carbonate depositional environment. Considering the correlation between the density and sonic logs, we determine the sonic log as input and the density log as output for the DNN. Although only five wells have a pair of training data in the field (i.e., sonic and density logs), we obtain, based on geological knowledge, 29 additional wells sharing the same depositional setting in the Slave Point Formation. After securing the data, 5 wells among the 29 wells are excluded from dataset during preprocessing procedures (elimination of abnormal data and min–max normalisation) to improve the prediction model. Two cases are designed according to usage of the well information at the target field. Case 1 uses only 23 of the surrounding wells to train the prediction model, and another surrounding well is used for model testing. In Case 1, the Levenberg–Marquardt algorithm shows a fast and reliable performance and the numbers of neurons in the two hidden layers are of 45 and 14, respectively. In Case 2, the 24 surrounding wells and four wells from the target field are used to train the DNN with the optimised parameters from Case 1. The synthetic density logs from Case 2 mitigate an underestimation problem in Case 1 and follow the overall trend of the true density logs. The developed prediction model utilises the sonic log for generating the synthetic density log, and a reliable porosity model will be created by combining the given and the synthetic density logs. [ABSTRACT FROM AUTHOR]