Acoustic Logging Research Articles

In Situ Stress Determination Based on Acoustic Image Logs and Borehole Measurements in the In-Adaoui and Bourarhat Hydrocarbon Fields, Eastern Algeria

The study of in situ stress from image logs is a key factor for understanding regional stresses and the exploitation of hydrocarbon resources. This work presents a comprehensive geomechanical analysis of two eastern Algerian hydrocarbon fields to infer the magnitudes of principal stress components and stress field orientation. Acoustic image logs and borehole measurements were used in this research to aid our understanding of regional stress and field development. The studied In-Adaoui and Bourarhat fields encompass a combined thickness of 3050 m of Paleozoic and Mesozoic stratigraphy, with the primary reservoir facies in the Ordovician interval. The Ordovician sandstone reservoir interval indicates an average Poisson’s ratio (v) of 0.3, 100–150 MPa UCS, and 27–52 GPa Young’s modulus (E). Direct formation pressure measurements indicate that the sandstone reservoir is in a hydrostatic pore pressure regime. Density-derived vertical stress had a 1.1 PSI/feet gradient. Minimum horizontal stress modeled from both Poisson’s ratio and an effective stress ratio-based approach yielded an average 0.82 PSI/feet gradient, as validated with the leak-off test data. Drilling-induced tensile fractures (DITF) and compressive failures, i.e., breakouts (BO), were identified from acoustic image logs. On the basis of the DITF criterion, the maximum horizontal stress gradient was found to be 1.57–1.71 PSI/feet, while the BO width-derived gradient was 1.27–1.37 PSI/feet. Relative stress magnitudes indicate a strike-slip stress regime. A mean SHMax orientation of N130°E (NW-SE) was interpreted from the wellbore failures, classified as B-quality stress indicators following the World Stress Map (WSM) ranking scheme. The inferred stress magnitude and orientation were in agreement with the regional trend of the western Mediterranean region and provide a basis for field development and hydraulic fracturing in the low-permeable reservoir. On the basis of the geomechanical assessments, drilling and reservoir development strategies are discussed, and optimization opportunities are identified.

Machine learning-based cement integrity evaluation with a through-tubing logging experimental setup

Assessing the integrity of the cement layer and the quality of its bond to the casing and formation is paramount to ensure that the wellbore is hydraulically isolated from the surrounding environment before permanently sealing the well. Such inspection is part of Plugging & Abandonment (P&A) operations, and it is usually achieved through well logging tools, which provide a vast amount of data that a skilled specialist interprets. The process is human-dependent, error-prone, and time-consuming. There has been an increasing interest in solutions that allow an automatic interpretation of the logging data since the recent panorama of the oil and gas industry indicates a growing P&A demand. Such solutions aim at reducing the dependence on human knowledge and consequently increasing the reliability and accuracy of the cement integrity evaluation. Therefore, this work presents an experimental setup capable of emulating defective cement layer configurations of real-world oil wells in single or multi-string arrangements. Such flexibility enables acquiring logging data for multiple well conditions and building a rich logging database to develop a supervised learning framework and define the most suitable model for performing the cement integrity evaluation. Several logging runs were performed, producing a database with 130 samples, including varied tubing eccentricity levels and cement layer conditions. A complete analysis of the data both in the time domain as well as in the frequency–wavenumber domain was performed, highlighting the complexity of the interpretation task. A resampling-based workflow was employed to evaluate machine learning models of different families. The models were tested under three scenarios, and accuracy and computational complexity metrics were computed to compare their performance. The results showed that shallow learning models can perform satisfactorily well even with less data available for training. The support vector machine stood out, achieving a mean accuracy score higher than 0.99 while being able to predict the cement sheath’s condition in less than 1 ms. This paper contributes to the research on the cement integrity evaluation by presenting a study that combines an experimental setup mimicking several oil well conditions and the employment of machine learning as a diagnostic tool, which has no precedents in recent literature regarding the acoustic logging knowledge field.

Automatic facies classification from acoustic image logs using deep neural networks

Borehole image logs greatly facilitate a detailed characterization of rock formations, especially for the highly heterogeneous and anisotropic carbonate rocks. However, interpreting image logs requires massive time and workforce and lacks consistency and repeatability because it relies heavily on a human interpreter’s expertise, experience, and alertness. Thus, we propose to train an end-to-end deep neural network (DNN) for instant and consistent facies classification of carbonate rocks from acoustic image logs and gamma-ray logs. The DNN is modified from the well-known U-Net for image segmentation. The training data are composed of two data sets: (1) manually labeled field data measured by different imaging tools from the geologically complex Brazilian presalt region and (2) noise-free synthetic data. Some short sections of the field data that are challenging for manual labeling due to entangled features and noise or low resolution are left unlabeled for a blind test after training. All labeled data are divided into a training set, a validation set, and a test set to avoid overfitting. We determine that the trained DNN achieves a 77% classification accuracy for the test set and provides reasonable predictions for the challenging unlabeled sets. It is a great achievement given the complexity and variability of the field data. Compared with manual classification, our DNN provides more consistent and higher-resolution predictions in a highly efficient manner and thus dramatically contributes to an automatic image log interpretation.

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

The accurate estimation of reservoir porosity plays a vital role in estimating the amount of hydrocarbon reserves and evaluating the economic potential of a reservoir. It also aids decision making during the exploration and development phases of oil and gas fields. This study evaluates the integration of artificial intelligence techniques, conventional well logs, and core analysis for the accurate prediction of porosity in carbonate reservoirs. In general, carbonate reservoirs are characterized by their complex pore systems, with the wide spatial variation and highly nonlinear nature of their petrophysical properties. Therefore, they require detailed well-log interpretations to accurately estimate their properties, making them good candidates for the application of machine learning techniques. Accordingly, a large database of (2100) well-log records and core-porosity measurements were integrated with four state-of-the-art machine learning techniques (multilayer perceptron artificial neural network, MLP-ANN; Gaussian process regression, GPR; least squares gradient boosting ensemble, LS-Boost; and radial basis function neural network, RBF-NN) for the prediction of reservoir porosity. The well-log data used in this study include sonic acoustic travel time, Gamma-ray, and bulk density log records, which were carefully collected from five wells in a carbonate reservoir. This study revealed that all the artificial intelligence models achieved high accuracy, with R-squared values exceeding 90% during both the training and blind-testing phases. Among the AI models examined, the GPR model outperformed the others in terms of the R-squared values, root-mean-square error (RMSE), and coefficient of variation of the root-mean-square error (CVRMSE). Furthermore, this study introduces an artificially intelligent AI-based correlation for the estimation of reservoir porosity from well-log data; this correlation was developed using an in-house, Fortran-coded MLP-ANN model presented herein. This AI-based correlation gave a promising level of accuracy, with R-squared values of 92% and 90% for the training and blind-testing datasets, respectively. This correlation can serve as an accurate and easy-to-use tool for porosity prediction without any prior experience in utilizing or implementing machine learning models.

A Three-Dimensional Display Technology of Reflected Acoustic Logging and Its Application Example

Reflection acoustic logging is a new technology for identifying geological structures around the well. It can be used to describe the geological structure, such as formation interface, fracture, and limestone cave. Based on the acoustic logging tool composed of the source and array receiver, the method can be used to image the geological structure around the well. Through migration imaging, the section crossing the well can be obtained in any azimuth. In the current three-dimensional (3D) display technology of reflection acoustic logging, the reflection plane around the well is always displayed as a reflection arc. The arc surface correction method can correct the arc surface in 3D imaging into a plane. According to the similarity criterion in a physical experiment and the need for practical research, we designed a scale model well experiment of dipole reflection acoustic logging. We obtained the 3D display of the reflection structure around the well by the experiment and realized the arc surface correction of the 3D display in reflection acoustic logging. The application shows the method can qualitatively describe the lateral extension length of the geological structure around the well. After the arc surface correction, the geological structure can be more accurately described.

Electrical Interference Simulation and Prediction Model for Acoustoelectric Logging Detector.

Acoustic logging instruments generate high voltages in the order of thousands of volts. Electrical interferences are thus induced by high-voltage pulses that affect the logging tool and make it inoperable owing to damaged components in severe cases. High-voltage pulses from the acoustoelectric logging detector interfere with the electrode measurement loop through capacitive coupling, which has seriously affected the acoustoelectric signal measurements. In this paper, we simulate high voltage pulses, capacitive coupling and electrode measurement loops based on qualitative analysis of the causes of electrical interference. Based on the structure of the acoustoelectric logging detector and the logging environment, an electrical interference simulation and prediction model was developed to quantify the characteristics of the electrical interference signal.

Effective elastic properties and S-wave anisotropy for rocks containing any oriented penny-shaped cracks in transversely isotropic background

Fractures have a significant influence on rock elastic properties. The existing elastic models for cracked medium mainly consider the crack dip angle. In this work, based on the previous transversely isotropic (TI) background and arbitrary dip crack model, a theoretical model is derived using the Bond transform matrix to quantify the effects of any oriented penny-shaped cracks on the elastic properties of rocks with a TI background. Based on this model, the effects of crack orientation on rock elastic properties are studied. Particularly, a special case with random crack orientation is investigated by using the spherical averaging of the crack compliance matrix with any orientations. Furthermore, the S-wave anisotropy for rocks containing multiple crack sets also is analyzed. The results indicate that the crack orientation has a great influence on rock elastic properties. Especially, the rock elastic properties indicate nonlinear changes with crack azimuth angles when cracks are inclined with respect to the TI background isotropic plane. The randomly oriented cracks decrease the overall rock elastic moduli but have little effect on rock anisotropy. For rocks containing multiple crack sets, parallel crack sets have great effects on the S-wave anisotropy, whereas the effects of orthogonal crack sets are negligible. Using this model, a complete workflow for the inversion of fracture properties using field data measured by acoustic logging is introduced. To explain the effect of crack azimuth angle, the crack density is inverted without considering crack orientation, only considering crack dip angle, and considering both crack dip and azimuth angles. The results indicate that the inversion results considering the crack dip and azimuth angles are in good agreement with the resistivity imaging results. This work provides the theoretical basis for fracture properties inversion for rocks with a TI background.

Classification and well-log identification of lithofacies of continental shale oil reservoirs in Chang 7-3 submember of the Triassic Yanchang Formation in the Longdong area of the Ordos Basin, China

The division and identification of lithology and lithofacies are very important in exploration and development of oil and gas reservoirs. Information on lithofacies determines the development modes and provides the favorable “sweet spot” intervals of shales. The Chang 7-3 submember of the Triassic Yanchang Formation in the Longdong area of the Ordos Basin is an important shale formation in China. Core photos, cast thin sections, and log response characteristics were used to clarify the development and distribution characteristics of the various lithofacies in this formation. The lithofacies of target intervals are classified into fine-grained sandy debrite, silty to fine-grained sandy turbidite, fine-grained sandy slump, semideep to deep lacustrine mudstone, and deep lacustrine shale. Furthermore, the conventional and microresistivity imaging log response characteristics of different lithofacies are analyzed. Compared with other lithofacies, deep lacustrine shale facies are characterized by high gamma ray (GR), high compensated neutron log (CNL), high acoustic log (AC), and low-density (DEN) log values. Crossplots of GR-AC, DEN-CNL, and DEN-AC are proposed to identify most of the lithofacies, and a criterion was established for use of well logs to characterize the lithofacies. Finally, a method joining conventional logs and microresistivity imaging log data was applied to field wells, achieving the very high accuracy rate of 88%. The distribution maps of different lithofacies thickness in the study area are obtained. Deep lacustrine shale facies have the widest development area and many thickness centers. Accurate and effective identification of lithofacies indicates that within the Ordos Basin, there is an area of approximately 20,000 km2 with shale thickness of greater than 6 m, providing an important basis for shale oil exploration in the study area.

An Unsupervised Machine-Learning Workflow for Outlier Detection and Log Editing With Prediction Uncertainty

Recent advances in data science and machine learning (ML) have brought the benefits of these technologies closer to the mainstream of petrophysics. ML systems, where decisions and self-checks are made by carefully designed algorithms, in addition to executing typical tasks such as classification and regression, offer efficient and liberating solutions to the modern petrophysicist. The outline of such a system and its application in the form of a multilevel workflow to a 59-well multifield study are presented in this paper. The main objective of the workflow is to identify outliers in bulk density and compressional slowness logs and to reconstruct them using data-driven predictive models. A secondary objective of the project is to predict shear slowness in zones where such data do not exist. The system is fully automated, designed to optimize the use of all available data, and provide uncertainty estimates. It integrates modern concepts for outlier detection, predictive classification, and regression, as well as multidimensional scaling based on inter-well similarity. Benchmarking of ML results against those created by experienced petrophysicists shows that the ML workflow can provide high-quality answers that compare favorably to those produced by human experts. A second validation exercise, that compares acoustic impedance logs computed from ML answers to actual seismic data, provides further evidence for the accuracy of the ML-generated results. The ML system supports the petrophysicist by easing the burden on repetitive and burdensome quality control tasks. The efficiency gains and time savings created can be used for enhanced effective cross-discipline integration, collaboration, and further innovation.

Dolomite reservoir fluid discrimination method based on fluid identification factor

The complexity of the pore structure of dolomite leads to strong heterogeneity of the reservoir distribution and the inability to accurately describe the rock-electric parameters, which makes it difficult to distinguish the properties of reservoir fluids based on electrical logging. In this paper, fluid identification factors are applied to dolomite reservoirs for the first time, which enriches the methods of non-electric logging to identify reservoir fluid properties. Based on the inductive analysis of existing fluid identification factors, according to the number of times that logging data participates in the calculation, they are divided into three types of fluid identification factors: direct, indirect and composite; composite fluid identification factors can reflect changes in reservoir lithology, and reconstruct composite fluid identification factor H considering the difference between the lateral deformation coefficient of dolomite and other lithologies; the sensitivity of the composite fluid identification factor to gas saturation is tested, and fluid factors used in dolomite reservoirs were optimized. Select the array acoustic logging and density logging data from three wells in the subsalt dolomite in the Tarim Basin, China, calculate the parameters of the compressional wave and shear wave impedances (Ip, Is), λρ, μρ, ρf, F, H and etc., and use the two-dimensional chart method to analyze the effect of the fluid identification factors above, and λρ-μρ, F-σ, ρf-σ, H-μ and other charts are selected to establish logging fluid discriminant curves. The application shows that the fluid identification factor H is more suitable for distinguishing gas layers, and F is more suitable for distinguishing water layers.

A hybrid CNN-LSTM machine learning model for rock mechanical parameters evaluation

With the continuous deepening of hydrocarbon exploration and development, the exploration proportion of unconventional fields has gradually increased, and unconventional energy has become the main component of oil and gas production. Rock mechanical parameters are key to the efficient development of unconventional fields. However, rock mechanical parameters are mostly obtained from expensive measurements such as coring. In this study, a hybrid CNN-LSTM machine learning model is proposed to obtain rock mechanical parameters efficiently through predicting acoustic log in a highly accurate manner. The model incorporates a hybrid multi-channel CNN-LSTM network, which has the advantage of capturing the temporal and spatial features of logs. A group of feature enhancement methods are also proposed and incorporated into the model to improve the reliability of prediction. In this work, the model is utilized to predict acoustic log. Static rock mechanical parameters, which are typically hard to obtain, could be calculated based on dynamic parameters derived from predicted acoustic log. The performance of model is verified by the data of three wells in Jimsar field, and the results show that the introduced model has the highest prediction accuracy for acoustic logs. And the static rock mechanical parameters evaluated via proposed model are subsequently validated against coring data. A good match is achieved, which demonstrates the feasibility of using this model to guide the efficient development of unconventional fields in future.

A novel evaluation scheme of resistivity anisotropy in near-tight sandstones using conventional geophysical logs: A case study of the Triassic Chang 8 oil layer, Zhenjing area, Ordos Basin

The resistivity anisotropy characteristic of near-tight sandstone reservoirs is important for reservoir evaluation, but it is a challenge to obtain it by using conventional logging curves in vertical boreholes. Thus, a novel evaluation scheme is established. Firstly, in the Chang 8 Formation of Zhenjing area, Ordos Basin, China, horizontal rock samples (parallel to bedding) and vertical rock samples (perpendicular to bedding) are collected for designed rock electricity experiments and analyzed to obtain the corresponding horizontal and vertical Archie's parameters. Secondly, based on the detection characteristics of acoustic logging instrument and the transformation form of the Archie's equation, an estimation method of vertical resistivity is established. Combined with the horizontal resistivity obtained from conventional deep resistivity logs in vertical boreholes, an estimation method of resistivity anisotropy coefficient is proposed. Thirdly, the relationships between resistivity anisotropy coefficients and water saturations and porosities are analyzed using experiment results, and four adjacent vertical wells in the study area are processed using the estimated method. The processed results demonstrate the feasible of the estimation method. In addition, the cross well profile of resistivity anisotropy coefficient in the target formation is characterized, which shows the target formation is slightly resistivity anisotropic and slightly heterogeneous. The relationships between the resistivity anisotropy coefficient and commonly used logs are discussed, which contribute to rapid analyze the resistivity anisotropy of the target formation. Based on the above methods and processes, the novel evaluation scheme of resistivity anisotropy is established, which contributes to quantify and qualitatively analyze the resistivity anisotropy characteristics of formations in and between vertical boreholes. The application results show that it provides an alternative scheme for continuously evaluating the resistivity anisotropy in boreholes and cross well profiles by using conventional logs and experiments.

Fracture characterization of Asmari Formation carbonate reservoirs in G Oilfield, Zagros Basin, Middle East

The Asmari Formation in the G oilfield on the Iran-Iraq border is a fractured-porous multi-lithology mixed reservoir, for which fracture is an important factor affecting oil productivity and water cut. The characterization and modeling of fractures in the carbonate reservoir of G oilfield are challenging due to weak conventional well log responses of fractures and a lack of specific logs, such as image logs. This study proposes an integrated approach for characterizing and modeling fractures in the carbonate reservoir. The features, formation mechanism, influencing factors, and prediction methods of fractures in the Asmari Formation carbonate reservoirs of G oilfield were studied using core observation, thin section, image log, cross-dipole acoustic log (CDAL), geomechanics numerical simulation (GNS), and production data. According to CDAL-based fracture density interpretation, GNS-based fracture intensity prediction between wells, and DFN-based rock fracture properties modeling, the quantitative fracture characterization for G oilfield was realized. This research shows that the fractures in the Asamri Formation are mainly medium-to high-angle shear fractures. The substantial compression stress during the Miocene played a major role in the formation of the prominent fractures and determined their trend in the region, with primary trends of NNW-SSE and NNE-SSW. The fracture distribution has regularity, and the fractures in zone A dolomites are more highly developed than that in zone B limestones vertically. Horizontally, fractures intensity is mainly controlled by faults and structural location. The results of this study may benefit the optimization of well design during field development. From 2019 to 2021, three horizontal wells pilot tests were deployed in the fractures belt in zone A, and these fractures prominently increased the permeability of tight dolomite reservoirs. The initial production of the wells is four to five times the average production of other wells in the area, showing a good development effect. Meanwhile, the updated numerical simulation validates that the history match accuracy of water cut based on the dual-porosity model is significantly improved, proving the fracture evaluation and prediction results to be relatively reliable and applicable.

Using a Synthetic Borehole Model to Determine the Pore Aspect Ratio Dependence of Velocities from Acoustic Well-Logging Data

Revealing the elastic wave properties of carbonate rocks with complex pore structures and improving the reliability of carbonate reservoir descriptions have always been a global challenge in the field of carbonate geophysical exploration. In this study, we established a synthetic borehole model by selecting different particle sizes of cement, carbonate cuttings, and micro-silicon as the matrix, and silicon disks as the pores in carbonate rocks. We conducted four sets of low-porosity (0-3%) borehole models with different pore aspect ratios (ARs) and measured the P- and S-wave velocities ( V P and V S ) at the well-logging scale obtained from an acoustic logging system with one source and two receivers. The results indicate that the relationship between velocities and porosities in these borehole models follows a linear relation, with the pore AR significantly influencing the velocities at any given porosity. The velocity variation caused by pore AR reaches 560 m/s and 410 m/s at 3% porosity for the P-wave and S-wave within the AR range of 0.017-0.13. The theoretical DEM models provide a high and broad estimation of V P and V S at the well-logging scale in our measurement. They could perform better in fractured formation than in dissolved porous formation in carbonate reservoirs. The linear relation of V P and V S is independent of the pore AR and is effective for both fractured and dissolved porous formations. The change of V P / V S in different pore AR is more responsive to porosity and nonlinear dependent on the pore AR. The relationship between the defined normalized V P and V S indicates the pore AR has a more significant effect on V S than V P in our model. The constructed borehole models provide a unique opportunity for evaluating the availability of rock physics models at an acoustic logging scale. The study’s findings have significant implications for improving the reliability of carbonate reservoir descriptions and enhancing the accuracy of geophysical exploration in carbonate rocks with complex pore structures.

Experimental Study on the Azimuthal-Transmitting Transducer of Acoustic Logging

Azimuthal acoustic logging can survey the downhole formation more accurately, and the acoustic source is the crucial component of the downhole acoustic logging tool with azimuthal resolution characteristics. To realize downhole azimuthal detection, assembling multiple transmitting piezoelectric vibrators in the circumferential direction is necessary, and the performance of azimuthal-transmitting piezoelectric vibrators needs attention. However, effective heating test and matching methods are not yet developed for downhole multi-azimuth transmitting transducers. Therefore, this paper proposes an experimental method to comprehensively evaluate downhole azimuthal transmitters; furthermore, we analyze the azimuthal-transmitting piezoelectric vibrator parameters. This paper presents a heating test apparatus and studies the admittance and driving responses of the vibrator at different temperatures. The transmitting piezoelectric vibrators showing a good consistency in the heating test were selected, and an underwater acoustic experiment was performed. The main lobe angle of the radiation beam, horizontal directivity, and radiation energy of the azimuthal vibrators and azimuthal subarray are measured. The peak-to-peak amplitude radiated from the azimuthal vibrator and the static capacitance increase with an increase in temperature. The resonant frequency first increases and then decreases slightly with an increase in temperature. After cooling to room temperature, the parameters of the vibrator are consistent with those before heating. Hence, this experimental study can provide a foundation for the design and matching selection of azimuthal-transmitting piezoelectric vibrators.

Research on Improvement Measures of Cementing Acoustic Amplitude Logging

Cementing is one of the important aspects in the exploration and development of oil and gas fields, and the quality of cementing has a great impact on the life, production capacity and overall efficiency of a well. Due to the limitations of the evaluation technology and the complexity of the influencing factors, the conventional method of cementing quality evaluation currently used in China often leads to misjudgment of the quality of the cemented wells and can no longer meet the requirements of high accuracy research. By analyzing the factors affecting the solids quality testing and the advantages and disadvantages of several common solids quality evaluation methods, and organically combining them according to the measurement principles and their usage correlations, a system of evaluation methods applicable to different requirements is formed, and the intelligent evaluation of solids quality is further explored to provide a concrete and practical basis for solids quality evaluation in the development and production of oil fields.

Excess Anisotropy: A Method to Predict Frequency of Resistive Fractures in the Wolfcamp Shale

A horizontal well in the Wolfcamp Formation within the Delaware Basin exhibits cross-dipole sonic log anisotropy varying from 0.0% to 29.7% over the reservoir interval. Massive limestones exhibit little anisotropy while shales and intervals with high fracture counts have high anisotropy. Integrated analysis of acoustic, image and triple combo open-hole log data indicates that the computed volume of shale log is a strong predictor of anisotropy with a simple linear relationship. However, there is much anisotropy that is not accounted for by this lithologically related linear relationship. We refer to the residual obtained by subtracting the predicted anisotropy using this relationship from the measured anisotropy from a cross-dipole sonic tool as the excess anisotropy. The excess anisotropy is the remaining anisotropy that is not accounted for by linear regression between the volume of shale log and the measured anisotropy. We find that the excess anisotropy is linearly correlated to the resistive fracture density. Linear regression between the two variables yields a statistically significant relationship that enables a rough estimation of the resistive fracture frequency from the excess anisotropy. Validation in a second well produces a similar correlation to fracture frequency.

Typical misinterpretations and scientific concepts in well-logging geologic studies

The method and theoretical system of well logging geology have been widely used in the fields of basic geology, petroleum geology and engineering geology, but the different response sensitivity of different well logging series to geological information and the mismatching between geophysical properties of multiple well logs and geological genesis of rocks frequently result in misunderstandings in the research process of well logging geology. Therefore, it is in an urgent need to analyze the typical misunderstanding cases in the research of well logging geology and explore the corresponding scientific ideas and countermeasures. After analyzing the typical misunderstandings in the research of well logging geology, this paper investigates vertical resolution scale of various logging series and its contradiction with detection depth and illustrates the importance of the integration of different scales of data. In addition, the factor inducing “fake logging data” and its influence on interpretation evaluation are clarified and a set of ideas for well logging evaluation of geological interpretation is put forward. And the following research results are obtained. First, the typical misunderstandings in the research of well logging geology can be classified into two categories, namely geological body interpretation misunderstanding and reservoir property parameter calculation misunderstanding. Second, special geological phenomena, such as high-density and high-resistivity mudstone can lead to logging data ambiguity, so attention shall be paid to petrophysical response mechanisms during geological logging interpretation. Third, to carry out well logging evaluation of unconventional oil and gas, it is necessary to integrate new technologies of electric imaging logging, dipole acoustic logging and nuclear magnetic resonance logging, and the calibration of core data and the integration of geological ideas can improve the interpretation accuracy. Fourth, In the process of borehole structural logging analysis, sedimentary response, geostress evaluation and fracture identification, geological ideas shall be integrated to avoid the logging interpretation misunderstanding caused by the same response of different geological phenomena in well logs. In conclusion, the dialectical and systematic thinking from geology to logging and then to geology, from practice to recognition and then to practice and from “a narrow view” to “a broad view” can provide a scientific ideas for the comprehensive research of well logging geology.

Improving Prediction of Fracture Distribution Using Microseismic Data and Acoustic Logging Measurements

Summary The complex fracture network from hydraulic fracturing can significantly improve oilwell productivity, so it is widely used in the field of unconventional reservoir development. However, accurate evaluation of the fracture spatial distribution remains a challenge. As a result, how to combine a variety of data to avoid data islands and identify and predict the space of fracture zone is of great importance. In this paper, we present a method and workflow based on the microseismic (MS) data combined with shear wave velocity data to estimate the physical parameters of subsurface media and improve the description and prediction accuracy for hydraulic fractures. The method analyzes MS events to construct the fracture spatial distribution and uses acoustic logging measurements to correct the magnitude of MS events and enhance the resolution. The corrected MS magnitude is mapped to the MS event space for Kriging interpolation analysis to predict the improved spatial distribution of fractures, which is available in the format of a 3D cloud image.

Detailed Geoseismic Structure of the Riphean Deposits in Buolkalakh–Lena Area Based on Deep Drilling and Seismic Exploration Data

Abstract —The work considered an integrated approach to the study of deep-lying Riphean strata of the Buolkalakh–Lena region, which have certain prospects in terms of oil and gas potential. The method of revision and synthesis of acoustic and density logs is described in detail using the example of wells drilled in 80–90 for the purpose of correct reference to seismic sections. It has been shown that the Khaipakh, Debengdin, Arymas, Kyutingdinsky, and Sygynakhtakh formations of the Early and Middle Riphean eras overlook the pre-Permian surface in the middle part of the Lena–Anabar trough. The region also developed older Riphean deposits that are not exposed on the Olenek uplift and are not studied by deep drilling. The prospects for the oil and gas content of Riphean strata in the region are mainly associated with carbonate reservoirs formed in the top of the Riphean sequence and cropping out on the pre-Permian erosive surface, the most promising rocks are those constituting large carbonate platforms that were partially eroded and subjected to processes of hypergenesis and karst formation.