Log Interpretation Research Articles

Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning

Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.

Enhancing Reservoir Zonation through Triple Porosity System: A Case Study

Summary The Asmari-Jahrom reservoirs, located in southwest Iran, are recognized as one of the major fractured reservoirs in the world. Understanding the role of fractures in enhancing hydrocarbon flow and permeability is of utmost importance. In this study, petrophysical conventional logs [neutron porosity (NPHI), density (RHOB), sonic (DT), and gamma ray (GR)] and advanced image logs [formation microresistivity imaging (FMI)] were used to investigate the reservoir properties. The novelty of this study lies in the implementation of triple porosity on reservoir quality and identification of flow units in Asmari-Jahrom reservoirs using petrophysical and borehole image logs. By quantifying fracture and vuggy porosity and correlating them with velocity deviation log and fracture parameters [fracture aperture (VAH) and fracture density (VDC)], it was demonstrated that fracture porosity is directly related to VAH. High peaks were observed in fracture parameters, particularly in VAH diagrams where the velocity deviation log was negative and low. Total porosity from density logs was found to match secondary porosity from petrophysical logs, validating FMI results. However, FMI log resolution was higher, enabling clearer identification of fracture porosity peaks. The velocity deviation log indicated that the predominant type of porosity in the reservoir was matrix (primary) porosity. However, fracture and vuggy porosity were also observed in certain zones. Based on indirect evidence such as drilling mud loss, porosity type (matrix, fracture, and vuggy), porosity amount, and oil saturation, 18 zones were identified to determine quality zones with appropriate reservoir quality. Asmari-Jahrum reservoirs were found to possess high storage and flow capacity. The presence of multiple fracture types, especially longitudinal fractures, contributed to the development of secondary porosity and enhanced flow unit quality. Despite their complexity, these fractured carbonate reservoirs were analyzed comprehensively through integrated petrophysical and FMI log interpretation, enabling optimized reservoir performance and facilitating hydrocarbon production.

Application of Multi-Near-Surface Geophysical Surveys for Geothermal Spring: A Case Study in Kon Tum Province, Vietnam

The occurrence of geothermal spring not only enhances human health, but also plays a crucial role in generating community’s revenue that boosts up the standard living of locals. An endeavor has been made to delineate the prospective areas of geothermal spring in Kon Tum province, Vietnam by the utilization of joint-geophysical techniques. The blended mode as mentioned is opted for its superiority in providing the rapidassesments of temperature in shallow temperature survey, comprehensive understanding the subsurface structure in electrical resistivity tomography in copporating with the validation in well log interpretation. The results found that the overlapping region of high temperature (35°C) region and low resistivity (200 Ωm) is observed in the east in the vicinity of Dlak Bla river, and it probably broadens to the west of the study area. The outcome of geophysical approaches strongly concur with the well log interpretation, revealing the presence of water-bearing zone encountered in fractured granite. The results also corroborated the existence of concealing fault F5 as a branch of the NE-SW fault F2 which is favorable for creating a route to form the geothermal spring. The output of this research could be considered as an intentive consultation for Kon Tum’s planners and makers on expanding the investigation and exploitation.

Sedimentary microfacies of Member 5 of Xujiahe Formation in the Dongfengchang area, Sichuan Basin

Member 5 of the Upper Triassic Xujiahe Formation (T3X5) in central Sichuan Basin has made a breakthrough in exploration recently. However, this new stratum has not been investigated sufficiently with respect to basic geology, making its types and distribution of sedimentary facies unclear, which severely restricts its subsequent exploration evaluation. In this study, types of sedimentary microfacies in the first sand group of T3X5 (T3X51) are clarified through core observation and logging interpretation using core, log and seismic data, and then distribution of sedimentary microfacies in T3X51 is determined according to seismic waveform features and seismic prediction. The results show that T3X51 in the Dongfengchang area is mainly composed of deltaic deposits of several microfacies, such as delta front underwater distributary channel, sheet sand, and interdistributary bay. On seismic sections, different microfacies are significantly different in waveform features, the underwater distributary channel is characterized by one trough between two peaks, while diversion bay exhibits chaotic reflections between T6 and T51. The sedimentary microfacies varied greatly during the depositional period of T3X51 in the Dongfengchang area, this is because that the sediment supply was mainly controlled by the southwest and southeast provenance regions. Three superimposed underwater distributary channels are developed in the Dongfengchang area. The phase-1 superimposed underwater distributary channel in the northwest transition to sheet sand in the northeast, the phase-2 superimposed underwater distributary channel in the south extends shortly, the phase-3 superimposed underwater distributary channel in the northeast has a large development scale. These research findings are helpful to guide the subsequent exploration of T3X5 gas reservoir and also theoretically significant for investigating the depositional evolution of the Xujiahe Formation in central Sichuan Basin.

Application of the dynamic transformer model with well logging data for formation porosity prediction

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, the Dynamic Transformer model in machine learning was introduced in this study, aiming to improve the accuracy and generalization ability of logging porosity prediction. Dynamic Transformer is a deep learning model based on the self-attention mechanism. Compared with traditional sequence models, Dynamic Transformer has a better ability to process time series data and is able to focus on different parts of the input sequence in different locations, so as to better capture global information and long-term dependencies. This is a significant advantage for logging tasks with complex geological structures and time series data. In addition, the model introduces Dynamic Convolution Kernels to increase the model coupling, so that the model can better understand the dependencies between different positions in the input sequence. The introduction of this module aims to enhance the model's ability to model long-distance dependence in sequences, thereby improving its performance. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the Dynamic Transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the Dynamic Transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

Laboratory and Theoretical Investigations of Petroleum Reservoir Rock Properties

The main objective of this study is to compare the characteristics obtained from the laboratory with the characteristics obtained from well logs and experimental correlations and determine the percentage of error in each method. Core Analysis of reservoir rock is operation of conducting laboratory tests on the core samples to determine their physical and petro physical properties. The Rock sample is first cleaned to be ready for laboratory test then it is used to obtain parameters of rock properties. Well logging is the practice of making a detailed record of the geologic formations penetrated by the well. The log may be either geological logs or geophysical logs in geothermal. Techlog Software is one of the most valuable formation evaluation software’s used by geologist and reservoir engineers. It is routinely used to evaluate well and field performance, and reservoir characteristics. After evaluating the results, the crude sample was characterized by using hydraulic unit technique its performing to estimate the units inside the reservoir and establish the correlations to estimate the permeability for each unit. The New Correlations can be used to evaluate the permeability nearby wells. In this study Four wells were selected from different locations in 103A which are A7, A11, A16, and A21 They are used to estimate the petro physical properties of the reservoir by both core analysis and well logging interpretation; the petro physical properties were evaluated like porosity, permeability, and water saturation.

Empirically informed convolutional neural network model for logging curve calibration

Environmental calibration of logging curves is critical for petrophysical interpretation and sweet spot characterization. Wellbore failure frequently occurs in clay-rich shale rocks during drilling, leading to biased logging interpretation and uncertainty. To reduce the biased correction or erroneous decision making in the interpreter-dominated logging curve calibration process, we develop an empirically informed convolutional neural network (EiCNN) logging curve correction strategy to calibrate the borehole failure-induced logging curve abnormity more accurately. The EiCNN method, together with high-quality logging curves as labeled samples, provides a nonlinear mapping between input logging curves and calibrations for the distorted curves. The EiCNN method completely alleviates biased correction or decision making by the interpreter-dominated method. It has a strong generalization ability, using many empirically interpreted high-quality data as input samples. The field validation wells demonstrate that the EiCNN model can precisely correct the distorted logging curves of mudstone segments with a correlation coefficient of >0.95. Moreover, the validation and test wells illustrate that the EiCNN method is capable of precisely correcting logging curves of interlayer mudstone, implying that the EiCNN method, to a certain degree, can also accurately perform environmental correction of logging curves from thin mudstone layers.

Examples of Paleokarst in Mesozoic Carbonate Formations in the Carpathian Foreland Area

A paleokarst system developed in the Upper Jurassic–Lower Cretaceous carbonate complex has been recognized in the Carpathian Foreland area. Well logs testing and core data as well as an acoustic imager, a microresistivity scanner and six-arm dipmeter images were used to identify and analyze the character of the paleokarst features. A detailed interpretation of microresistivity and acoustic image logs allowed for the identification of different types of karst forms, such as caverns; multidirectional fractures, including fractures widened by dissolution; and the type of sediments filling them. The analysis of the seismic survey was conducted by linking the paleokarst characteristic features recognized in the seismic image to the karst intervals determined from borehole data. The set of seismic attributes calculated from the analyzed 3D seismic data, including the RMS amplitude, instantaneous frequency, consistent dip, variance, sweetness and relative acoustic impedance, helped to delineate the zones of the paleokarst distribution. Within the interpreted paleokarst surface developed in the carbonate formations in the study area, there are sinkholes, limestone pavements and valleys. Furthermore, in the northwestern part of the analyzed area, the development of paleokarst forms is related to the presence of a relatively deep branch of a paleovalley formed in the Paleogene, as well as to numerous discontinuities developed in carbonate formations. The development of this type of larger karst form was probably controlled primarily by tectonic faults. The research conducted by the authors of this paper showed the widespread presence of paleokarst features in Upper Jurassic–Lower Cretaceous carbonate formations in the study area. A good spatial identification of the paleokarst surface can be important in a regional context, since the highest part of the profile of carbonate formations is the most important reservoir for geothermal or hydrocarbon resources in this region.

Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, this study introduces a CNN (convolutional neural network)-transformer model, which aims to improve the accuracy and generalization ability of logging porosity prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation of CNNs can effectively learn the mapping relationship of local features, so as to better capture the local correlation in the well log. Transformer models are able to effectively capture complex sequence relationships between different depths or time points. This enables the model to better integrate information from different depths or times, and improve the porosity prediction accuracy. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the CNN-transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the CNN-transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

MODELING of Water Saturation in Shallow Sandstone Oilfield

3D geological modeling technology has achieved visualization of underground reservoirs in oil and gas fields, and has been widely applied in oil and gas field development. 3D geological modeling includes structural modeling and attribute modeling, and strata and faults can be completed based on data from oil and gas field exploration and development. The porosity and permeability in the geological attribute model can be established based on logging interpretation conclusions and other relevant data, The establishment of a saturation model has always been a difficulty in 3D geological modelling. As the water saturation, is different from porosity and permeability of an oil field. Saturation is distributed under the control of factors such as gravity and capillary force, and cannot be established through spatial simulation interpolation. Instead, the influence of factors such as gravity and capillary force should be considered. This article takes the shallow sandstone M oilfield in Kazakhstan as an example, and establishes the J function of the oilfield by combining the capillary pressure experiment of the core well, in order to establish an oilfield saturation model.

Structural and Stratigraphic Reservoir Characterization of “MONI” Field¸ Niger Delta.

Reservoir characterization of “MONI” field of the Niger Delta has been carried out using well log and seismic data. The study is aimed at delineating structural and stratigraphic features associated with hydrocarbon entrapment of the field. Lithostratigraphic correlation reviewed two main lithologies which are reservoir sands and seal shales. Well log correlation and interpretation across the field indicates – main stratigraphic bounding surface which include two sequence boundaries and one maximum flooding surface. This indicates about three depositional sequences exists in the study area. Stacking patterns (progradation, retrogradation, and aggradation) were delineated and interpreted as lowstand system tract (LST), highst and system tract (HST), and transgressive system tract (TST). The alternation of LST, HST and TST offers good reservoir and seal for the hydrocarbon accumulation. The depositional environment of the reservoirs ranges from subaerial channel deposits, barrier bar, tidal channel sands to delta distributary channel fill. Subaerial channel deposits have a bad reservoir quality, tidal channel sands possess a fair reservoir quality while delta distributary channel fill have a good reservoir quality. Petrophysical analysis shows that three reservoirs delineated have a porosity ranging from 19.37% to 23.62%, permeability ranging from 1408.71 to 1855.36 mD, a Net-to-Gross ranging from 0.46 to 0.88, volume of shale ranging from 11.72% to 24.92%, Water saturation ranging from 37.24% to 47.96% and hydrocarbon saturation ranging from 52.04% to 62.76%. Structural interpretation reviewed four main faults that serves as traps to the accumulation of hydrocarbon. Three hydrocarbon bearing sands were interpreted in the seismic volume. Time and depth structure maps show that the hydrocarbon bearing structure is a fault assisted anticlinal structure. Seismic facies analysis reviewed five facies. The study concluded that structural and stratigraphic features can be used to characterize reservoirs.

Well Log Analysis and Interpretation for Mishrif Formation for Amarah Oil Field

Well Log Analysis and Interpretation for Mishrif Formation for Amarah Oil Field

Application of Secondary Logging Interpretation—Taking Yan 9 Reservoir in X Area as an Example

Application of Secondary Logging Interpretation—Taking Yan 9 Reservoir in X Area as an Example

The present-day in-situ stress field and its effect on shale gas development in Zigong area of southern Sichuan Basin

The Zigong area in southern Sichuan Basin is one of the key shale gas production areas in China, and the Longmaxi and Wufeng Formations within it are currently the key exploration and development layers. The present-day in-situ stress field has a significant impact on well trajectory deployment, horizontal well construction, hydraulic fracturing and other aspects. However, it has not been finely quantified in shale gas reservoirs of Zigong area, making it difficult to effectively guide development practice. Therefore, this study constructed a geomechanical model of the target layer in the Zigong area, quantitatively characterized the distribution of present-day in-situ stress, and explored its shale gas development effects. The results show that: (1) Based on the analysis of drilling induced fractures obtained from rock acoustic emission experiments and imaging logging interpretation, the dominant orientation of the maximum horizontal principal stress in the target layer is mainly NW-SE-trending; (2) The horizontal principal stress difference in the Layer S1l1-1−1 and Wufeng Formation is 5–30 MPa (the majority is 6–15 MPa) and 5–35 MPa (the majority is 6–18 MPa), respectively. Low stress difference values are mainly concentrated in the southeastern and northern parts. The overall prediction accuracy is high, with an error rate of less than 8% for the Layer S1l1-1−1 and less than 10% for the Wufeng Formation; (3) Based on rock acoustic emission experiments and finite element model simulation results, the study area is mainly under strike-slip stress faulting mechanism; (4) Due to the influence of the strike-slip faulting stress mechanism, when deploying horizontal wells in the area, priority should be given to selecting areas with low stress mechanism factor (Aφ) and low stress differences, to achieve better fracturing and transformation effects and reduce the risk of casing deformation and wellbore instability.

Reservoir Characterization of Jeribe and Euphrates Formations in Qaiyarah OilField

Three-dimensional static model is a crucial key for a reservoir heterogeneity representation and an initial evaluation of the hydrocarbon in place, thus for a success field development, an accurate reservoir characterization is a fingerprint for the decision making. The initial goal of this study is to generate a static model for Jeribe and Euphrates formations in Qaiyarah oil field to investigate the distribution of the discrete and continuous proprieties such as, permeability and water saturation, in sequence, recognizing the value of the reserve to identity the ability of developing this reservoir as a final decision for future investment. The sever hetrogeneity and complexity of Qaiyarah oil field were the main challenges confronted through the static model building, in consequence, this paper repesents a fingerprint for the future studies in this area. The conceptual methodology in this research includes the integration of the well log data and final well reports to construct the three-dimensional model through log interpretation, sequential simulators, and properties upscaling. Initial volumetric calculations were performed mainly based on the petrophysical properties distribution of eight wells and oil water contact level. Accordingly, the hydrocarbon volume in place were estimated in the two pay reservoirs under study Jeribe and Euphrates formations. The results showed that Euphrates Formation has dominated by the largest portion of reverse (554*106 Sm3), while Jeribe Formation has lower volume of oil initially in place about 194 *106 Sm3.

Optimization of gas saturation calculation model for deep clastic reservoirs under strong compression stress

The interpretation model for gas saturation in deep clastic reservoirs subject to intensive extrusion stress relies predominantly on experimental petrophysical data and the expanded Archie equation. Nonetheless, these methodologies may not consistently yield optimum results. Specifically, when subsurface pressure impacts formation resistivity, the resulting curve values exhibit substantial elevations, leading to significant inaccuracies in the precise estimation of gas saturation from resistivity logging data. Consequently, the evaluation of logging interpretations becomes a complex undertaking. This paper aims to comprehensively assess gas saturation in deep clastic reservoirs from both electrical and mechanical perspectives. To achieve this goal, we employ a multifaceted approach encompassing the Archie formula, the stress-corrected variable cementation index saturation model, and the resistivity-corrected saturation model. Through rigorous theoretical analysis and the utilization of simulated experimental data, a quantitative relationship equation between resistivity and stress difference has been established. Building upon this fundamental groundwork, an innovative resistivity-corrected saturation calculation model has been proposed. In comparison to the two alternative models, the new model exhibits enhanced accuracy in calculating gas saturation, as it is less influenced by extrusion stress and gravitational compaction. Furthermore, it demonstrates better consistency with core mercury injection capillary pressure data in determining gas saturation. The findings of this research provide valuable insights for the effective evaluation of deep clastic reservoirs, offering a robust framework for advancing the understanding of gas saturation in the face of complex geological and geophysical challenges.

Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability. In view of the sedimentation, diagenesis, testing, and production characteristics of carbonate reservoirs in the study area, combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs, a log interpretation technology route of “geological information constraint + deep learning” was developed. The principal component analysis (PCA) was employed to establish lithology identification criteria with an accuracy of 91%. The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%. Based on production data, the main lithologies and sedimentary microfacies of effective reservoirs were determined, and 10 petrophysical facies with effective reservoir characteristics were identified. Constrained by petrophysical facies, the mean interpretation error of porosity compared to core analysis results is 2.7%, and the ratio of interpreted permeability to core analysis is within one order of magnitude, averaging 3.6. The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data. Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

Investigation of Source and Reservoir Rock Petroleum Generative Potential of Early Eocene and Paleocene Sequences of the Punjab Platform Basin, Pakistan: A Geochemical and Petrophysical Approach

Abstract ––Investigating Early Eocene and Paleocene deposits within the Punjab Platform, Pakistan, involved comprehensive analysis using advanced geochemical and petrophysical techniques. This study evaluates the source potential of three formations: Nammal and Sakesar of Early Eocene age and Dungan and Ranikot of Palaeocene age. The Sakesar Formation, a reservoir rock, was evaluated through interpretation of wireline logs to estimate key petrophysical properties—porosity, water saturation, and hydrocarbon saturation. Geochemical investigations focused on total organic carbon content and Rock-Eval pyrolysis of twenty-one cutting samples from a well. These samples displayed TOC values ranging from 0.21 to 2.04 wt.% and S2 values ranging from 0.09 to 2.14 mg HC/g rock. Analysis positioned all samples within the immature window zone with Type II/III and Type III kerogen, suggesting limited generative potential. Findings suggest limited hydrocarbon generation from these formations. Petrophysical analysis indicated average porosity of 11.2%, water saturation of 32.58%, and hydrocarbon saturation of 67.42% for the Sakesar Formation, categorizing it as average to good reservoir potential. This study highlights complex geochemical and petrophysical characteristics of these formations. While Sakesar shows promising reservoir qualities, significant hydrocarbon generation potential from Nammal, Dungan, and Ranikot formations is constrained. Informed decision-making is crucial when considering these formations as hydrocarbon sources.

Features of geophysical monitoring of current oil and gas saturation of productive formations

The process of developing oil and gas fields requires continuous monitoring of the current oil and gas saturation of productive reservoirs. Monitoring the current oil and gas saturation allows for effective hydrocarbon recovery. Solving this problem under the conditions of a cased hole significantly limits the suite of geophysical methods, which are the main source of information about the conditional characteristics of reservoirs. The most effective geophysical methods for determining the current oil and gas saturation of a productive reservoir in a producing well is deemed to be the methods of pulse neutron logging. In this paper, the authors analyzed the features of this method, the geological and technological conditions for its effective application, and identified factors that significantly affect the reliability of log interpretation. The indications of pulse neutron-neutron logging are influenced not only by the geological characteristics of the studied reservoir. During the operation and production of an oil and gas well, technological factors also appear, which often significantly change the methods’ indications. Scientists have identified most of these factors and proposed ways to account for their influence. In their work, the authors focused on two factors that are often neglected when performing logging using pulsed neutron methods, accordingly, and not taking them into account during interpretation often led to misinterpretation of pulsed neutron-neutron logging readings when determining the current oil and gas saturation. There are cases of reservoir contamination during drilling and cementing of the casing, when an unusually large colmatage zone is formed. Or the near-wellbore zone of the reservoir formation is damaged during well operation. Given the limited radial resolution of pulsed neutron methods, an enlargement in the colmatage zone or damaging of the downhole zone of the reservoir during well operation has a significant impact on the method readings. The authors proposed a comprehensive approach that allows avoiding such errors

Fluid Identification Method of Nuclear Magnetic Resonance and Array Acoustic Logging for Complex Oil and Water Layers in Tight Sandstone Reservoir

In order to improve the logging interpretation accuracy for complex oil and water layers developed in tight sandstone reservoirs, this study takes the Chang 8 member of the Yanchang Formation in the Huanxian area as the research object, and two new fluid identification methods were constructed based on nuclear magnetic resonance (NMR) logging and array acoustic logging. Firstly, the reservoir characteristics of physical properties and conductivity were studied in the research area, and the limitations of conventional logging methods in identifying complex oil and water layers were clarified. Then, the sensitive parameters for identifying different pore fluids were established by analyzing the relationship between NMR logging and array acoustic logging with different pore fluids. On this basis, the fluid identification plate, composed of movable fluid apparent diffusion coefficient and effective porosity difference (Da-Δφe) by NMR logging data of D9TWE3 observation mode, and the other fluid identification plate, composed of apparent bulk modulus of pore fluid and elastic parameter sensitive factor (Kf-Fac), were constructed, respectively. Finally, these two fluid identification methods were used for reservoir interpretation of actual logging data. This study shows that the two new fluid identification methods constructed by NMR logging and array acoustic logging can effectively eliminate the interference of rock skeleton on logging interpretation, which make them more effective in identifying complex oil and water layers than the conventional logging method. Additionally, the two methods have their own advantages and disadvantages when used separately for interpreting complex oil and water layers, and the comprehensive interpretation of the two methods provides a technical development direction for further improving the accuracy of logging the interpretation of complex oil and water layers.