Logging Methods Research Articles

Identification of watered-out zones in carbonate reservoirs using resistivity curve reconstruction based on reservoir classification

The KT–II layer in the Zananor Oilfield, Caspian Basin, Kazakhstan, contains carbonate reservoirs of various types. The complex pore structure of the reservoirs have made it difficult to identify watered-out zones with traditional logging interpretation methods. This study classifies the reservoirs on the basis of core analysis and establishes an identification model for watered-out layers in the field to effectively improve the interpretation accuracy. Thin section analysis shows that there are three types of pores in the reservoirs, i.e., the matrix pore, fracture and dissolution vug. A triple porosity model is used to calculate the porosities of the reservoirs and the results are combined with core analysis to classify the reservoirs into the fractured, matrix pore, fracture-pore as well as composite types. A classification standard is also proposed. There are differences in resistivity logging responses from the reservoirs of different types before and after watering-out. The pre–watering-out resistivities are reconstructed using generalized neural network for different types of reservoirs. The watered-out layers can be effectively identified according to the difference in resistivity curves before and after watering-out. The results show that the watered-out layers identified with the method are consistent with measured data, thus serving as a reference for the evaluation of watered-out layers in the study area.

Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine

Resistivity low-contrast oil pays are a kind of unconventional oil resource with no obvious difference in physical and electrical properties from water layers, which makes it difficult to be identified based on the characteristics of the geophysical well logging response. In this study, the support vector machine (SVM) technology was used to interpret the resistivity low-contrast oil pays in Chang 8 tight sandstone reservoir of Huanxian area, Ordos Basin. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, these two models were applied to interpret the resistivity low-contrast oil pays in the study area. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method, back propagation neural network method and radial basis function neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the resistivity low-contrast oil pays by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.

Development Characteristics and Finite Element Simulation of Fractures in Tight Oil Sandstone Reservoirs of Yanchang Formation in Western Ordos Basin

Structural fractures have a significant control effect on the large-scale accumulation of hydrocarbons in the Yanchang Formation. Previous studies have affirmed the important role of fractures in hydrocarbon accumulations in strongly deformed zones. However, for low-amplitude structural areas, the degree of fracture development is relatively low, and their control on sweet spots of hydrocarbons has not yet formed a unified understanding. In this paper, taking the Upper Triassic Yanchang Formation in the western Ordos Basin as an example, the development characteristics, prediction method, and the distribution of fractures in tight sandstone reservoirs in low-amplitude structural areas have been systematically studied using a large number of cores, thin sections, paleomagnetism, FMI logging, acoustic emission, productivity data, and finite element method. The research results showed that the Yanchang Formation in the study area mainly develop high-angle and vertical fractures, which were formed by regional tectonic shearing. Fractures are mainly developed in the fine-grained and ultra-fine-grained sandstones of the distributary channel and estuary bar microfacies, while the fractures in the medium-grained sandstones of the distributary channel and the mudstones of the distributary bay are relatively underdeveloped. The core fractures and micro-fractures of the Yanchang Formation all have the regional distribution characteristics, and the fracture strikes are mainly between NE50° and NE 70°. Moreover, the finite element method was used to predict the fractures in the target layer, and the prediction results are consistent with the actual distribution results of the fractures. The coupling analysis of fractures and tight oil sandstone distribution showed that the existence of fractures provided conditions for the accumulation of hydrocarbons in the Yanchang Formation. The confluence and turning areas of the river channels were repeatedly scoured by river water, and the rocks were brittle and easy to form fractures. The thickness of the fractured sandstone in these areas is usually greater than 0.4 m. Moderately developed fracture zones are prone to form hydrocarbon accumulation “sweet spots,” and the fracture indexes of these areas are usually distributed between 0.8 and 1.2. However, when the fracture index exceeds 1.2, over-developed fractures are unfavorable for the accumulation of hydrocarbons.

Novel Fluid Typing Method for Nmr Dual-Tw Logging in Mixed-Wet Reservoirs

Novel Fluid Typing Method for Nmr Dual-Tw Logging in Mixed-Wet Reservoirs

РЕЗУЛЬТАТИ ГЕОФІЗИЧНОГО МОНІТОРИНГУ РОЗРОБЛЕННЯ ГОРИЗОНТУ ПК-2 ПІДКИРМАКІНСЬКОЇ СВІТИ РОДОВИЩА НАФТИ ДАШЛАРИ

The article is devoted to the study of the current state of development of the second horizon of the Podkirmakinsky suite (PK-2) of the Productive stratum (PS) of the Neft Dashlary field, which has been in operation for more than 60 years. The results of the study by the method of complex areal interpretation of geophysical survey data of wells and geological and field information are presented. Three-dimensional geo models, built on the basis of petrophysical parameters obtained after the interpretation of geophysical and geological data using the DV Seys Geo program are given in the article. The results of the analysis of the state of water impact are presented, the values of the oil saturation factor during well drilling are summarized, the values of the current oil saturation factor are calculated taking into account the extraction of oil, the results of studying the nature of saturation of reservoirs in the context of production wells by the method of pulsed neutron logging (PNL), as well as a comparative analysis of these values along the horizon PK-2 of the PS of the Neft Dashlary field.

An optimized XGBoost method for predicting reservoir porosity using petrophysical logs

To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm is introduced to construct a model for porosity prediction, and the obtained model is optimized by the grid search method and genetic algorithm. First, the optimal values of the three integer hyperparameters and the ranges of optimal values for the five floating-point hyperparameters of the XGBoost algorithm are determined by the grid search method. Then, the optimal values of the five floating-point hyperparameters of the XGBoost algorithm are determined with the genetic algorithm based on the determined value ranges. In this way, the model for porosity prediction based on the XGBoost algorithm and optimized by the grid search method and genetic algorithm (GS-GA-XGBoost) is constructed, and it has eight hyperparameters with determined optimal values. Compared with other porosity prediction methods, our method solves the problems such as the strong subjectivity and poor generalizability of conventional logging interpretation methods and the insufficient generalization performance of machine learning methods in previous porosity prediction studies, and the accuracy of the constructed model for porosity prediction is also greatly improved. Specifically, the RMSE, MAE and MAPE generated by GS-GA-XGBoost on the test set are 0.527946, 0.155880 and 0.020500 respectively, while those generated by linear regression (LR), support vector regression (SVR), random forest (RF), the XGBoost algorithm with default parameters (XGBoost) and the XGBoost algorithm optimized only by the grid search method (GS-XGBoost) on the test set are 3.535521, 2.801047, 0.375713, and 2.695310, 2.002280, 0.283582, and 1.015801, 0.638878, 0.085942, and 2.781069, 1.860557, 0.293334, and 1.380065, 0.979419, 0.128486, respectively. Finally, the multithread technology is introduced to improve the computational speed of GS-GA-XGBoost. GS-GA-XGBoost provides some technical references for the construction of porosity prediction models for oil fields in Northern Shaanxi, China, and other regions.

A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data

Brittleness is a significant mechanical property which determines the degree of fracturing difficulty in shale gas exploitation and hence, the brittleness index (BI) is a key parameter for the selection of suitable hydraulic fracturing intervals in shale gas reservoirs. However, at present there are no universal and convenient methods for the prediction of BI. Therefore, this study proposes a well logging data-driven BI prediction method based on principal component analysis (PCA) and back-propagation neural networks (BPNN), which can interpret nonlinear and complex relationships between predictors (input variables) and predicted values (target variables). Specifically, 63 samples and their corresponding well logging data were collected from the Wufeng-Longmaxi and Baota formations at the periphery of Sichuan Basin. Experimentally measured brittleness index (BIcore) values (from 0.18 to 0.88) derived from compression test stress-strain curves, were considered as the target parameter. Based on the results of linear regression and sensitivity analysis, five types of well logging, including gamma ray (GR), density (DEN), compressional wave slowness (DT), neutron porosity (CNL) and spontaneous potential (SP), were selected as appropriate well logging techniques. Three principal components were extracted from the five well logging datasets using PCA. Following PCA, the back-propagation neural network (PCA-BPNN) model exhibited excellent prediction accuracy according to statistical and graphical error analysis. The established PCA-BPNN model was then applied to forecast the BI profiles of the intervals of interest in well Y1 in the study area and the predicted BI values were found to be in agreement with the laboratory measured BIcore values. Therefore, the established PCA-BPNN BI prediction method can be applied to accurately locate suitable hydraulic fracturing intervals for economic and technical practicality.

Comprehensive logging identification method of shell limestone fractures in the Lower Jurassic Da'anzhai Member in the Dongpo area of the Western Sichuan Depression

Shell limestone tight reservoirs are developed in the Lower Jurassic Da'anzhai Member of the Sichuan Basin. Fractures are important storage spaces and seepage channels in shell limestone tight oil reservoirs and are key factors for high oil and gas production in wells. In this study, considering the shell limestone reservoir of the Da'anzhai Member on the eastern slope of the Western Sichuan Depression as an example, we used multiple fracture interpretation models based on conventional logging to identify fractures. Our results show that: (1) the reservoirs in the Da'anzhai Member primarily showed low-angle structural fractures, mostly filled with calcite. The effective linear density of fractures in each well varied greatly, which primarily explains the strong heterogeneity of tight reservoirs. (2) The logging response characteristics of the fractured and non-fractured segments differ considerably, and the fractured segment shows the characteristics of high acoustic time difference and low resistivity. (3) The well log recombination method, principal component analysis method, multivariate discriminant method, backpropagation (BP) neural network method, and K-nearest neighbor (KNN) algorithm are used for fracture identification. According to the consistency of the fracture identification results with the core observation results, we established a comprehensive fracture identification standard using the multi-logging method. In this study, we established a fracture identification method based on the coupling of multiple linear and nonlinear multi-logging models through core calibration, which avoids the errors and uncertainties caused by using a single method. Additionally, this study provides a foundation for the control factors and the prediction of fracture distribution.

Interval inversion based well log analysis assisted by petrophysical laboratory measurements for evaluating tight gas formations in Derecske through, Pannonian basin, east Hungary

The evaluation of Hungarian tight gas formations requires the use of innovative interpretation technologies because of diverse geological environment, heterogeneous dataset including several variables measured with different accuracies, and reservoirs under varying petrophysical conditions. Conventional well logging methods based on the use of limited number of wireline logs make only a quick-look interpretation, in which a limited number of unknowns is determined, and there is a non-negligible risk for occurrence of ambiguity. In order to increase the overdetermination (data-to-unknowns) ratio and the estimation accuracy and reliability, all suitable well logs are to be jointly processed in a more effective manner. To estimate the total organic carbon content together with porosity, water saturation, shale content and rock matrix volumes with higher accuracy, a new inversion methodology is introduced that is tested in tight gas reservoirs. A differential evolution algorithm-based interval inversion method is developed that is combined by nano-permeability and high precision porosity laboratory measurements for the detection and evaluation of Hungarian tight gas reservoirs. Spectral gamma-ray, neutron-porosity and electric resistivity logs are jointly analyzed using the interval inversion approach. By solving a highly overdetermined inverse problem, the effective porosity, water- and gas saturation, shale and sand content, as well as the kerogen volume are estimated in a reliable inversion procedure. The silt and clay content of Miocene tight gas formations are separated and both determined by inversion, while the total organic carbon content is derived from the inversion results. The permeability log is directly derived from the inverted porosity values using the local porosity versus permeability relation established by multi-well core analysis. The combination of in situ wireline log data analysis and porosity and nano-permeability laboratory measurements is shown to be advantageous for a more reliable interpretation of unconventional hydrocarbon formations. An added advantage of the interval inversion method is the calculation of depth dependent errors of the estimated petrophysical quantities. It is shown that total organic carbon and other volumetric parameters can be determined with high accuracy in one interpretation procedure. In the Derecske basin, East Hungary, gas indications are confirmed by interval inversion, that were previously made it probable according to measurements made on poor core samples and rather noise-sensitive local well logging inversion procedure. The joint evaluation of new core data and original wireline logs using the proposed methodology may reveal further potential tight gas resources in the studied area. The Hungarian case study confirms that the suggested workflow may serve a new alternative for an improved evaluation of tight gas formations in different other hydrocarbon fields.

Reliability of results of use of complex geophysical studies in the process of control of development of oil and gas deposits

The paper considers modern methods for determining the position of gas-liquid contacts in terrigenous deposits at oil and gas condensate fields in Ukraine. The results of laboratory studies of a representative core collection (87 samples) in the petrophysical laboratory of IFNTUOG made it possible to establish the reservoir parameters and lithological characteristics of reservoir rocks of Carboniferous and Neogene sediments, which made it possible to reliably estimate the reservoir parameters of productive formations. The results of well studies, in particular, obtained during the isolation of oil and gas-saturated reservoir rocks and monitoring the dynamics of changes in oil-water and gas-water contacts, made it possible to form an optimal informative complex of geophysical methods for effective prospecting and development of oil and gas fields. Direct studies of the identified reservoir rocks made it possible to establish that the geological sections of the Jurassic, Cretaceous and Neogene systems of prospecting areas are filled with both hydrophilic and hydrophobic lithotypes of rocks, which have different effects on the readings of electrical and radioactive methods. Reasonably, the maximum value of the electrical resistivity, the maximum oil saturation coefficient (Кo), as well as the minimum value of the water saturation coefficient (Кw), which is typical for oil and gas-saturated formations, must correspond to the condition for displaying these parameters in completely water-saturated rocks. In this case, the boundary of the transition zone will be displayed by the bottom of the maximum radio-saturated layer and the top of the aquifer in this rock. Confirmation of the above is seen from the fragments of geophysical data processing, electrical and neutron studies in the presence of low and high salinity, drilling fluids and drilling fluids. The analysis of the efficiency of using the methods of pulsed neutron-neutron logging (PNNL) and neutron gamma-ray logging (NGL) when monitoring the change in the position of the GLC to prevent watering of productive formations is carried out. According to the research results, it is proposed to use a complex of nuclear physical methods in the process of monitoring the dynamics of changes in NWC, in particular, neutron gamma logging, repeated neutron logging, as well as in the process of drilling conducting electrical methods of imaginary resistance and unauthorized potentials.

Long-term Trend in Mean Density of Antarctic Krill (Euphausia superba) Uncertain

Two recent attempts to model the long-term trend in mean density of Antarctic krill in the southwestern sector of the Atlantic using the KRILLBASE dataset using different statistical methods as well as inclusion versus exclusion of data from “non-scientific” nets have resulted in disparate conclusions. The approach that used a linear mixed model (LMM) fitted to the log of mean density, after standardisation was applied to individual net hauls and with means calculated for 12 spatial strata by years between 1976 and 2016, gave a highly statistically significant linear “regional” decline north of 60oS and, to a lesser degree, south of this latitude. The alternative approach that used a ”hurdle” model fitted to the individual net haul data, excluded regional stratification, and excluded non-scientific nets failed to detect an overall significant decline. The method of modelling log transformed means was reappraised and corrected by applying a meta-analytic LMM approach. Additionally, nonlinear smooths in year by region and a smooth in mean “climatological temperature” were included in the LMM. This model showed on average a mostly consistent decline north of 60oS, however, neither trend was significantly different from a no-trend prediction with the trend north of 60oS highly uncertain. Uncertainty of predictions resulted in only weak power to detect a substantial decline of the order of 70% between 1985 and 2005. These model-based inferences neither strongly support nor reject a general hypothesis that there has been a dramatic decline in density of Antarctic krill in the Southwest Atlantic over this period.

Application of radio-wave geointoscopy method to study the nature of spreading the solutions in the process of uranium underground leaching

Purpose. Assessment of the effectiveness of using the method of radio-wave geointoscopy of the inter-well space for three-dimensional mapping of the zone of the leaching solution actual propagation in the process of uranium mining by the method of underground leaching. Methods. Experimental-industrial studies of the leaching process are conducted at technological block 68 of the Semizbay deposit (Kazakhstan). In experimental studies, special equipment is used for conducting radio-wave geointoscopy. Inter-well measurements are performed using the RVGI-06 equipment. The observations are conducted in a fan pattern within the filter section. The step between adjacent points along the wellbore is 1 m. At different stages of mining the technological block, maps of geoelectric resistivity have been compiled, with the help of which a comparative analysis is performed. Findings. A tendency to an increase in the area of acidic solutions propagation over time has been revealed by comparing the fragments of RVGI geoelectric map at different stages of mining the block. The influence of a heterogeneous geological structure on the uniformity of the leaching solutions propagation has been proved. It has been determined that the resolving power of the radio-wave geointoscopy method is sufficient to detect changes in geoelectric conditions at small monitoring cycles in time. The spatial-temporal change in the front of the leaching solutions propagation makes it possible to determine the prevailing directions of solutions propagation and to assess the filtration characteristics of rocks. Originality. The patterns have been determined of the leaching solutions propagation over time from the beginning of block acidification to active leaching. The first attempts have been made to use the geophysical well logging method in the practice of uranium mining by In-Situ Leaching (ISL) method. Practical implications. Monitoring studies by radio-wave geointoscopy method at the stage of passive acidification can be re-commended for further experimental and scientific testing at technological blocks of the Semizbay deposit for a quantitative assessment of the filtration characteristics of rocks and the dynamics of the acidification process development, as well as for the development of well-grounded recommendations on the optimal scheme for mining the blocks in specific geotechnical conditions.

НЕЙРОСЕТЕВОЙ ПОДХОД К ЭКСПРЕСС-МОДЕЛИРОВАНИЮ СИГНАЛОВ ЭЛЕКТРОКАРОТАЖА В РЕАЛИСТИЧНЫХ МОДЕЛЯХ СЛОЖНОПОСТРОЕННЫХ ТЕРРИГЕННЫХ ОТЛОЖЕНИЙ

The article proposes a new algorithmic approach to resistivity logs simulation based on convolutional neural networks wich allows constructing algorithms for solving forward problems for specific logging tools in detailed models of near-wellbore space with thin layers, accounting for radial resistivity changes, borehole wall irregularities and drilling fluid displacement by the logging tool. Experimental algorithms for expressmodeling for three common Russian galvanic and induсtion logging methods in two-dimensional models of the near-wellbore space have been implemented based on the proposed approach. Logs simulation using the developed neural network algorithms is multi pletimes faster than using numerical solvers. The proposed solutions open up possibilities to use more sophisticated basic geoelectric models of the near-wellbore space. The use of models adequate in complexity to the actual target geological objects will increase the reliability of interpretation results of resistivity logs measured in complex geological conditions.

Novel metaphor translation is modulated by translation direction

AbstractPrevious translation process research has pointed to an increased cognitive load when translating metaphoric compared to literal language. Yet, studies conducted thus far have not examined the role of translation direction (i.e., L1–L2 vs. L2–L1) in novel metaphor translation and have not tested whether and how this process might be modulated by the linguistic form of a novel meaning. In the present study, Polish (L1) – English (L2) translation students translated novel nominal metaphors (A is B), novel similes (A is like B), and literal sentences, in either L1–L2 or L2–L1 translation directions, while their translation behavior was recorded using a keystroke logging method. The results revealed longer translation durations for both metaphors and similes relative to literal utterances. Furthermore, we found slower translation times for novel nominal metaphors compared to novel similes and literal sentences, yet only in the L2–L1 translation direction. Such results might indicate that novel meaning translation is more cognitively taxing in the case of novel nominal metaphors, which require a more robust activation of comparison mechanisms, relative to novel similes. Importantly, this effect might be stronger when translating in the direction in which access to semantic representations is potentially more automatic (i.e., L2–L1 translation).

Characteristics of Terrestrial Heat Flow and Lithospheric Thermal Structure in Typical Intermountain Basin at the Eastern Foot of Yanshan Mountain, North China

The Yanshan area is rich in geothermal resources. However, limited research has been done on the geothermal resources of intermountain basins in the area, which restrict the exploration and development of geothermal resources. In this study, the Yanheying Basin, a typical intermountain basin located in the eastern foothills of Yanshan Mountain, is selected to perform a comprehensive analysis regarding heat flow and lithospheric thermal structure, to have a better understanding of the geothermal background and resource utilization potential of the area. The methods of deep borehole temperature logging, and rock sampling and testing were applied. With geological and geophysical data collected in the surrounding basins, the thermophysical parameters of rock formations, terrestrial heat flow and temperature distribution on a crustal-scale around the basin were analyzed. Results show that the ratio of crust heat flow to that of the mantle in the Yanheying Basin is 0.68, which falls in the range of that of the North China Plain (0.6–0.8), showing an obvious result of lithospheric thinning. The results also show that both crust and mantle heat flows in the Yanheying Basin are far lower than those of the North China Plain. it indicates that the crust of the Yanheying Basin and the entire Yanshan area has experienced a low degree of damage. That provides a piece of good thermodynamic evidence for the spatial variation of North China Craton destruction. The depth of the Curie surface in the study area is estimated to be about 24 km, which is consistent with the results of aeromagnetic data analysis. It confirms the results of lithospheric thermal structure and deep temperature distribution in the Yanheying Basin. The Yanheying Basin has certain potential for geothermal resources utilization in the depth of 2–6 km, where probably exist several carbonate thermal reservoirs with medium-temperature geothermal water. The above results can provide new insights into the geothermal research and exploration of intermountain basins in the Yanshan area.

Trial performance of the zero waste harvesting method in three forest concession companies, Central Kalimantan Province, Indonesia

Currently, natural forest management companies (concession permit of timber forest products utilization-natural forest/IUPHHK-HA) have implemented Reduced Impact Logging (RIL) technology but timber harvesting is still carried out using the conventional method. Tree length logging is an alternative method of harvesting wood in an effort to reduced waste and increases the efficiency of the utilization of timber forest products and the preservation of natural production forests. The potential for wood harvesting waste is estimated at 2.21 million m3year−1 and has remained left in the cutting compartment for decades. The purpose of this paper is to obtain technical and financial information about forest harvesting on a zero waste basis. Data collection was carried out by experimental methods. Productivity, efficiency, cost, and stand damage data were collected. The results of the research in three IUPHHK-HA in Central Kalimantan show that the zero waste logging method can reduced harvesting waste by 5.1% and increase the efficiency of wood utilization from 87.7-92.8%. Another advantage of the zero waste logging method is that the additional waste from the trunk above the first branch is about 5.8%. The productivity of the conventional method averaged 26.333 m3hour−1 at a cost of IDR 33,941 m−3 while the tree length logging method was 27.320 m3hour−1 at a cost of IDR 35,251 m−3. It is suggested that the tree length logging method be implemented in natural production forest harvesting.

Research on prediction method of S-wave velocity based on deep learning

Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.

A New Evaluation Method of Total Organic Carbon for Shale Source Rock Based on the Effective Medium Conductivity Theory

In the evaluation of source rocks, the total organic carbon (TOC) is an important indicator to evaluate the hydrocarbon generation potential of source rocks. At present, the commonly used methods for assessing TOC include △ log R and neural network method. However, practice shows that these methods have limitations in the application of unconventional intervals of sand-shale interbeds, and they cannot sufficiently reflect the variation of TOC in the vertical direction. Therefore, a total organic carbon (TOC) evaluation model suitable for shale and tight sandstone was established based on the effective medium symmetrical conduction theory. The model consists of four components: nonconductive matrix particles, clay minerals, organic components (solid organic matter and hydrocarbons), and pore water. The conductive phase in the model includes clay minerals and pore water, and other components are treated as nonconductive phases. When describing the conductivity of rock, each component in the model is completely symmetrical, and anisotropic characteristics of each component are considered. The model parameters are determined through the optimization method, and the bisection iteration method is used to solve the model equation. Compared with the classic TOC calculation method, the new model can evaluate the abundance of organic matter in shale and tight sandstone, which provides a new option to assess the TOC of rocks based on logging methods.

Quantitative Interpretation of Water Sensitivity Based on Well Log Data: A Case of a Conglomerate Reservoir in the Karamay Oil Field

Abstract In the process of oil reservoir development, clay minerals in the pores of a reservoir swell because of water absorption, blocking the pore channels, and decreasing the reservoir permeability. This phenomenon is referred to as reservoir water sensitivity. To develop and protect oil and gas fields more scientifically and effectively and save development cost, we examine the water sensitivity of the low-permeability conglomerate reservoir of the Lower Karamay Formation in the 530-well area of District 8 of the Karamay oil field. Based on multiple linear regression and neural network methods, the quantitative calculation of different types of clay minerals was performed by comprehensively using natural gamma ray, neutron, density, sonic transit time, and X-ray diffraction (XRD) analysis data. The water sensitivity experiment was performed in the study area, and the logging interpretation method of reservoir water sensitivity was established by considering the clay mineral content as the medium; moreover, the reservoir water sensitivity degree was quantitatively explained.

A new method for logging identification of fluid properties in tight sandstone gas reservoirs based on gray correlation weight analysis — A case study of the Middle Jurassic Shaximiao Formation on the eastern slope of the Western Sichuan Depression, China

Energy exploration is becoming increasingly complex worldwide, and tight sandstone gas is an important field for the future development of the oil and gas industry. For the reservoir properties of the Shaximiao Gas Reservoir on the eastern slope of the Western Sichuan Depression in the Sichuan Basin, western China, it was found that the low-resistance characteristics of the reservoir complicate the gray characteristics among reservoir fluid property parameters. Some commonly used fluid property identification techniques, such as the flow zone index method, correlation analysis method of logging parameters, and traditional mathematical statistical methods, have poor fluid property evaluation results. Therefore, how to eliminate the influence of the gray features among the reservoir parameters on the identification of reservoir fluid properties and how to accurately identify the reservoir fluid properties are urgent problems that need to be solved. In this paper, we have developed a new method for identifying the fluid properties of tight sandstone reservoirs by combining gray system theory and multivariate statistical theory. This method can perform gray correlation weight analysis on parameters (combined parameters) closely related to fluid properties; furthermore, the logging identification method based on gray correlation weight analysis is used to identify reservoir fluid properties. The results indicate that the gray correlation weight analysis can accurately characterize the gray characteristics of reservoir fluid parameters and that the gray comprehensive correlation weight results are in good agreement with the production status of the studied gas reservoir. We used the method to identify the fluid properties of the target layer in 58 wells in the study area, and the discrimination rate of the model was 86.5%. In addition, the new model was used to predict the reservoir fluid properties of 12 newly drilled wells in the study area and the accuracy of the reservoir fluid property prediction was 91.67%.