Resistivity Image Logs Research Articles

Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

Abstract Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the Dung Beetle Optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-Means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the Dung Beetle Optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, Principal Component Analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.

Image Restoration of Electrical Well Logging Based on Fourier Convolution

Imaging logging is an important technical means in logging evaluation of complex reservoirs. Through imaging logging, a two-dimensional image of the resistivity distribution around the well can be obtained, which can be used to evaluate the development of well wall fractures and caves and the formation sedimentary structure. However, due to the characteristics of resistivity imaging logging instruments, blank strips will appear on the resistivity logging image, which increases the difficulty of computer processing of electrical imaging data. The current image repair method and the existing neural network image repair method are not effective enough when the part to be filled is large. Therefore, there is an urgent need for an intelligent repair method based on deep learning. Based on a fast Fourier convolution-based imaging logging image blank strip filling network, this paper constructs the electrical imaging logging images of the southwest oil and gas field as a data set, and trains a new deep learning algorithm for intelligent filling of blank strips in imaging logging images based on fast Fourier convolution. The time of various algorithms is compared. The results show that the algorithm has a better repair effect in imaging logging images with large strip widths, and the repair efficiency is significantly improved. This method can realize the rapid, accurate and intelligent repair of blank strips in imaging logging, achieve the rapid generation of full-borehole images, and solve the difficulties in obtaining full-borehole images.

Integrated Insights into Lithofacies, Fracture Networks, and Reservoir Productivity in Basement Reservoirs: A Comprehensive Case Study from the Cauvery Basin, India, Using Well Logs, Petrographic Studies, and Production Logs

Summary The architecture and heterogeneity of basement rocks can play a crucial role in reservoir evaluation and hydrocarbon exploration. This study focuses on the comprehensive characterization of basement reservoir heterogeneities and fracture networks in the Cauvery Basin using a combination of petrophysical and geochemical analyses. The intricate interplay of lithological variations and chemical alteration processes in basement rocks significantly influences their complex nature. In this study, openhole log data acquired in the basement section from wells within the Cauvery Basin were analyzed. These log data were correlated with core studies to understand the lithofacies heterogeneities present within the basement reservoir. Furthermore, fracture data obtained from resistivity image log data were examined to understand the distribution and properties of fractures in relation to lithofacies heterogeneities. Notably, a higher fracture density was observed in the mafic-rich sections compared to the felsic-rich sections. The integration of resistivity image log data with lithofacies contributed to the understanding of the reservoir architecture. Additionally, elemental spectroscopy logging facilitated geochemical analysis, providing measurements of the elemental composition of basement, which were incorporated into the study. Production logging (PL) was performed in the study wells to ascertain the production from the basement reservoir. This analytical approach augmented the characterization of the basement reservoir and contributed to a holistic understanding of its properties. This study emphasizes the role of conducting a thorough analysis of petrophysical log responses in conjunction with detailed core studies to characterize basement reservoirs. The study successfully differentiated two distinct lithofacies units within the Cauvery Basin’s basement reservoirs: mafic-mineral-dominated and felsic-mineral-dominated and their impact on reservoir heterogeneities. Production log data validated hydrocarbon contributions primarily from felsic rocks, underscoring the importance of lithological variations.

Use of resistivity and density borehole image logs to identify and distribute facies in the Pikka unit — A case study from the Nanushuk Formation, North Slope, Alaska

Over the past few decades, the North Slope of Alaska has yielded several major hydrocarbon discoveries in the deltaic topsets of the Brookian Nanushuk Formation. Together, the Nanushuk topsets and genetically related foreset and bottomset beds of the Torok Formation comprise part of a giant clinothem system that prograded across the Colville Foreland Basin during the lower Cretaceous (the Aptian through the Cenomanian). The Nanushuk Topset Play contains stratigraphically trapped hydrocarbons within multiple fairways trending roughly north to south along the basin’s extent. Inside the Pikka Unit, the Nanushuk Formation represents a shelf-edge delta and attached shoreface system, defined primarily from sedimentological interpretations of conventional cores integrated with 3D seismic reflection data and well-log motifs. In this study, we build a structural and stratigraphic framework for the Nanushuk reservoir across the Pikka Unit through integrated observations of the borehole image logs, seismic data, and core. In the described workflow, the boundaries delimiting dip zones interpreted in the wells are correlated to higher order stratigraphic cycles within the Nanushuk clinothem zone. These boundaries are observed in seismic profiles and provide the basis of the structural framework. In addition, core facies interpretations are calibrated to CT-scan data to establish each facies character and vertical distribution within the wells. In the absence of core data, core-calibrated borehole images are instrumental in guiding sedimentological and structural interpretations. This extrapolation is dependent on the image log quality and resolution. The calibrated facies interpretations are applied to wells without core data and guided by seismic amplitude interpretation to extrapolate reservoir presence and distribution across the area.

Comparison between double caliper, imaging logs, and array sonic log for determining the in-situ stress direction: A case study from the ultra-deep fractured tight sandstone reservoirs, the Cretaceous Bashijiqike Formation in Keshen8 region of Kuqa depression, Tarim Basin, China

The tight sandstone in the Tarim Basin has the characteristics of large burial depth and development of nature fractures due to concentrated in-situ stress. Identifying the present-day in-situ stress orientation is important in hydrocarbon exploration and development, but also a key scientific question in understanding naturally fractured reservoirs. This paper presents a case study where we integrate various methods using wireline and image-log data, to identify present-day in-situ stress direction of ultra-deep fractured tight sandstone reservoirs, in the Kuqa depression. We discuss the formation mechanism of the elliptical borehole, compares the advantages and applicable conditions of the double caliper method, resistivity image logs and array sonic logs method. The well borehole diameter is measured orthogonally, then the ellipse is fitted, and the in-situ stress orientation is identified by the azimuth of the short-axis borehole, but it fails in the borehole expansion section, the fracture development section and the borehole collapse section. The micro-resistivity image logs method reveals the borehole breakouts azimuth, and also the strike of induced fractures, which are used to determine the orientation of in-situ stress. However, under water-based mud conditions, it's hard to distinguish natural fractures from induced fractures by image logs. Under oil-based mud conditions, the induced fractures are difficult to identify due to the compromised image quality. As for the sonic log, shear waves will split when passing through an anisotropic formation, shear waves will split during propagation, and the azimuth of fast shear waves is consistent with the orientation of in-situ stress. However, it is usually affected by the anisotropy caused by the excessively fast rotation of the well log tools, so that the azimuth of fast shear wave cannot effectively reflect the orientation of the in-situ stress. Based on comprehensive assessment and comparison, in this paper we propose a method integrating various logging data to identify the orientation of in-situ stress. Among various types of logging data, the breakouts azimuth identified by image logs is proved to be the most credible in identifying the orientation of in-situ stress, while using the direction of induced fractures under water-based mud conditions is also viable. However, the azimuth of the fast shear wave is consistent with the orientation of maximum in-situ stress only when the rotation speed of the logging tool is low. The caliper method can be used as a reference for verifying the other two methods. Using this integrated method to study the orientation of in-situ stress in the Keshen 8 trap, the results show that faults are an important factor affecting the direction of in-situ stress, while multi-level faults will produce superimposed effects that cause the current direction of in-situ stress to change.

Prediction of Reservoir Fracture Parameters Based on the Multi-Layer Perceptron Machine-Learning Method: A Case Study of Ordovician and Cambrian Carbonate Rocks in Nanpu Sag, Bohai Bay Basin, China

Developing a model that can accurately predict internal fractured reservoirs in the context of the ultra-low physical properties of carbonate rocks by only employing conventional mathematical methods can be very challenging. This process is challenging because the relationship between basic fracture parameters and the logging response in carbonate reservoirs has not been studied, and the traditional method lacks adaptability due to the complex relationship between basic fracture parameters and the logging response. However, data-driven approaches supplemented by machine learning algorithms based on multi-layer perceptrons (MLP) provide a more reliable solution to this challenge. In this paper, a classical fracture parameter evaluation data set is established using fracture porosity, fracture density, fracture length, and fracture width data that can be identified by resistivity and acoustic imaging logging. Another data set can be composed of different types of logs, and it can be used to identify reservoirs. Two different data sets were validated by regression task evaluation indicators in machine learning, and the correlation coefficient R2 is greater than 0.82. This means that the model accuracy of the algorithm can reach 82%. Combined with the comparison results of eight conventional machine learning algorithms, the reliability and application validity of the MLP model are verified. This method’s accuracy is also verified by oil test data, which show that the MLP machine-learning algorithm can effectively simulate the relationship between lithology and fracture development. In addition, it can be used to predict key exploration horizons before drilling. The relationship between lithology and fracture development degree is well-simulated by the MLP machine learning algorithm, which shows that the degree of fracture development is mainly affected by fractures, indicating that the method can be used to predict key exploration horizons before drilling.

High-quality fracture network mapping using high frequency logging while drilling (LWD) data: MSEEL case study

The Marcellus Shale and Energy Environmental Laboratory (MSEEL) provides a comprehensive dataset and field tests that can be used to study the significance of preexisting natural fractures in different subsurface engineering problems such as the effectiveness of the stimulation of an unconventional reservoir, optimized geothermal fluids movement and integrity of the CO2 storage site. Conventionally natural fracture intensity is obtained using sonic and micro-resistivity imaging logs. However, these techniques significantly suffer from two major deficiencies: Human bias in log interpretation and extremely long interpretation time. These two deficiencies are well-recognized in the industry; however, no standard procedures exist to address them. In this study, a new automated machine learning workflow (AMLW) is introduced that uses the LWD high-resolution acceleration data along the horizontal laterals to predict the natural fracture intensities originally obtained using sonic and micro-resistivity imaging. The accuracy and robustness of the new workflow to predict the near wellbore fracture intensities are tested using both regression and classification approaches. Both the regression and classification approaches were able to predict the fracture intensities with high accuracy (average Mean Squared Error of 0.0085 for regression and average accuracy of 0.94 in the confusion matrix for classification). We have shown that only 10%–15% of the labeled resistivity image log is required for training and validation of the machine-learning model. The Automated workflow resulted in K-Neighbors Regressor and classifier algorithms as the best algorithms with a 52.74% and 139.3% improvement in comparison to the Gradient Boosting Regression algorithm (i.e. the fifth best algorithm).

Anisotropy and fracture analysis for coalbed methane reservoir development in Bokaro coalfield, India

ABSTRACTThe low permeability of coal seams is a constraint in the efficient production of coalbed methane. However, the presence of natural fractures in coal enhances the permeability, and prior knowledge of sub‐surface fractures in coal seams is vital to identify the prospective seam. This paper investigates the anisotropy and identifies fractures by processing the advanced sonic and resistivity image logs to mitigate challenges in the reservoir. Anisotropy is estimated from the difference in the travel time between fast and slow shear waves. The application of Alford's rotation technique determines the fast shear wave polarization angle which is consistent with the fracture orientation along the NE–SW or NW–SE direction in coal seams. Moreover, the crossover of fast and slow shear waves in the slowness versus frequency plot indicates stress‐induced anisotropy that originates from fractures. Besides, drilling‐induced fractures observed along NE–SW in the resistivity image log indicate the maximum horizontal stress direction. Results from this study compare coal seams based on fractures in adopting better operational activities for optimized production and future geomechanical studies in the Bokaro coalfield situated in India.

Automatic fracture detection and characterization from unwrapped drill-core images using mask R–CNN

Drill cores provide reliable information about fractures in subsurface formations as they present a clear and direct view of fractures. Core observation and image log interpretation are usually integrated for fracture analysis of underground layers. There has been a strong move towards developing automated fracture detection methods, however, the focus has been on extracting fracture information from log images, such as acoustic or resistivity image logs. Such efforts using core images are significantly less. This paper presents a machine learning-based approach for automatic fracture recognition from unwrapped drill-core images. The proposed method applies a state-of-the-art convolutional neural network for object identification and segmentation. The study also investigates the feasibility of using synthetic fracture images for the training of a learning model. Synthetic data can provide an alternative to real data, and thus address data availability issues common for supervised machine learning applications. We first create two types of synthetic data by using masks of real fractures and creating sinusoidal shaped fractures. The trained model is evaluated on real core images from two boreholes and provides an average precision of approximately 95%. The identified fractures are further analyzed and compared to manually segmented fractures in terms of fracture dip angle and dip direction, which achieved average absolute errors of around 2° and 11°, respectively. Overall, the study presents a novel application of an advanced machine learning algorithm for fracture detection and analysis from unwrapped core images.

Fracture analysis using Stoneley waves in a coalbed methane reservoir

ABSTRACTThe production of coalbed methane gas depends upon various factors such as permeability, gas content, gas saturation and seam thickness. The low permeability in coal is a constraint in the gas extraction process. However, the presence of natural fractures and cleats in coal increases the permeability; therefore, its identification is necessary to establish the prospectivity. Hence, a methodology is used to process the Stoneley waveform in the Bokaro coalfield located in India to obtain slowness magnitude from the semblance analysis. The separation of directly transmitted and reflected Stoneley waves determines the reflection coefficient and attenuation, and its analysis emphasizes the presence of fractures. In coal seams, the average reflection coefficient ranges from 0.42 to 2.44 and attenuation ranges from 0.2 dB to 12.68 dB. The Stoneley slowness versus frequency plot shows a constant slowness with increasing frequency in the non‐fractured seams, whereas a dispersive trend is observed in the fractured seams. Fractures are also distinguished from the cross‐plot of compressional/shear versus Stoneley velocity. The observation of higher reflection coefficient, attenuation and slowness‐frequency dispersion in coal seams indicates extensive fractures/cleats that corroborate with the resistivity image log correlation. Thus, the study helps to distinguish between fractured and non‐fractured coal seams from the integrated approach of reflection coefficient, attenuation and dispersion analysis of Stoneley waves supported by the identification of fractures from resistivity image log for optimization in coalbed methane production in the development phase.

Quantitative evaluation of fracture porosity from dual laterlog based on deep learning method

Fracture porosity is one of the key parameters for characterizing fractured reservoirs. However, fracture porosity calculation is difficult with conventional logging data due to severe anisotropy of the reservoirs. To deal with the problem, the equivalent macroscopic anisotropic formation model based on dual laterolog (DLL) data is adopted to cyclically assign such parameters as bedrock resistivity (RB), fluid resistivity in fractures (RFL), fracture dip angle (FDA) and fracture thickness as well as fracture spacing, and to produce massive data for formation modeling. A large number of training data obtained through three dimensional finite element forward modeling and the functional relationship between DLL responses and fracture parameters that are trained and summarized by deep neural network, are combined to establish a new fast forward model for calculating DLL responses in fractured formations. A new fracture porosity inversion model for fractured reservoirs based on gradient optimization inversion algorithm combined with multi-initial inversion strategy is then proposed. While running the model, formation is divided into eight intervals according to bedrock resistivity and fracture dip angle from 0° to 90° is divided every 0.5° to improve the operation speed and efficiency. The results of numerical verification show that when bedrock resistivity is greater than 1000 Ω m, the mean absolute error (MAE) of fracture porosity inversion is 0.001658% for horizontal fractures, 0.00413% for intermediate fractures and 0.0027% for quasi-vertical fractures. When bedrock resistivity is between 100 Ω m and 1000 Ω m, MAE of fracture porosity inversion is 0.003% for horizontal fractures, 0.0034% for intermediate fractures and 0.00348% for quasi-vertical fractures. Fracture parameters determined by the fracture porosity inversion model with actual data are in good agreement with the results of micro resistivity imaging logging.

Stress heterogeneity in the Changning shale-gas field, southern Sichuan Basin: Implications for a hydraulic fracturing strategy

To evaluate the risk of induced seismicity due to hydraulic fracturing, a detailed stress analysis in a seismically active region, i.e., the Changning shale-gas field in the southern Sichuan Basin, was conducted utilizing sonic wave velocity logs and high-resolution electrical resistivity image logs of shale gas wells. Wells with relatively higher magnitudes of horizontal stresses are spatially located in the vicinity of thrust faults, in agreement with the stress concentration at the upper tip of the basement-involved thrust fault. The prevailing orientation of maximum horizontal principal stress (SHmax) is N110°–120°E on the southwestern Changning anticline, which aligns with the motion of the Sichuan Basin, as driven by the continuous eastward extrusion of the Tibet plateau, which initiated at approximately 50 Ma. However, the SHmax orientation is N30°–70°E along the northern Changning anticline, implying an inheritance of the N–S geodynamic compression during the Jurassic to Late Cretaceous. Drilling-induced tensile fractures primarily appear in compliant layers that are rich in clay or organic matter in wells located adjacent to thrust faults, suggesting that stiff layers can efficiently inhibit the hydrofracture growth height owing to more intense tectonic stress near thrust faults. A natural fault with a strike of approximately 30° to the SHmax orientation can be more easily activated by extreme pumping pressure in wells that are weakly affected by the stress concentration along the thrust fault. Nevertheless, a cyclic injection of fracturing fluid with a gradually increasing target pressure may effectively reduce the magnitude of induced seismicity.

Factors influencing the in-situ stress orientations in shales: A case study of the Wufeng-Longmaxi formations in the Jiaoshiba Area, southeastern Sichuan Basin, China

Determining the present-day stress orientation is essential for the exploration and development of shale gas. The Longmaxi Formation is the most significant shale gas reservoir in the Jiaoshiba Area of the Sichuan Basin, China. However, little is known about the orientation of the maximum horizontal stress (SHmax) and the factors influencing it within the formation. In this study, the orientation of the SHmax in the Jiaoshiba Area was determined based on observations of drilling-induced fractures on resistivity image logs. Then, the tectonic activity, the mechanical properties of the rock, and finite element simulations were studied in detail to determine the major factors influencing the SHmax orientation in the Jiaoshiba Area. The results show that the SHmax orientation within the study area ranges from nearly EW to NEE-SWW. The vertical variations of the in-situ stress orientation are controlled by the structural strength and the mechanical properties of the rock. In the area that has experienced intense tectonic movements, the deviation in the SHmax orientation (from 30° to 80°) was caused by the differences in the mechanical properties of the rocks. The finite element simulations indicate that the lateral variations in the SHmax orientation was mainly controlled by the faults. The stress rotation near the end of the fault is 14°–21° higher than that in the middle of the fault. Furthermore, the stress variation caused by the mechanical properties of the fault zone ranges from 12° to 18°, and this deviation increases as the Young's modulus of the fault zone decreases. In the study area, the azimuth deflection (18°–46°) of the SHmax orientation influenced by the NE faults is higher than that (12°–36°) influenced by the nearly NS faults, which is the main reason for the lateral stress rotation.

Geometry of layer-bound fractures based on fracture density and aperture-depth plots from resistivity image logs of deviated wells

High resolution borehole resistivity image logs provide valuable bedding and fracture data from subsurface rocks including fracture type, orientation and aperture. The present study shows that common average shape and aperture geometry of uniformly located layer-bound fractures can be inferred from scan-line density and aperture-depth plots using image log data from a deviated well.The proposed method extends the use of borehole image logs and allows 3-dimensional (3D) modeling of fractures and fractured layers from subsurface data. So far, such 3D fracture studies could only be conducted using outcrop data. The approach is quite robust because correspondence between fracture geometry and image log plots is independent of fracture length and orientation, and applies to any fracture shape and aperture geometry.Joint interpretation of fracture scan-line density and aperture-depth plots, and bedding picks from borehole images also helps differentiate isolated/dispersed, unbound and multilayer fractures. Abundance of fractures that breach bed boundaries gives an idea about the cohesive bonding strength of bedding contacts and pave the way to construct fracture stratigraphy with several fractured layers that are separated by interfaces of varying bonding strength or by ductile beds. 3D fracture geometry may also provide some information on the type and mode of fractures and geo-mechanical conditions under which the fractures were generated.

Using logging while drilling resistivity imaging data to quantitatively evaluate fracture aperture based on numerical simulation

Abstract Fractured formations are strongly heterogenous, and thus exhibit a complex logging response mechanism. By using the logging while drilling (LWD) resistivity imaging tool, fractures can be visually identified and their aperture quantitatively calculated. Because physical fracture model simulation is time consuming and costly, we propose using a 3D finite element method (FEM) numerical simulation to interpret the LWD resistivity imaging tool logging responses in conjunction with a new aperture calculation model based on the forward model. First, we used the single fracture model to investigate the effect of fracture aperture and formation resistivity contrast on the maximum current contrast at the fracture. The results showed that the aperture is linearly related to the maximum current contrast, while the formation resistivity contrast exhibits a pronounced exponential relationship with the maximum current contrast. Both of these relationships are affected by the fracture's dip angle, so segmented fitting is required when the fracture dip angles differ. Next, using the forward model, we developed the fracture aperture calculation model based on the maximum current contrast. The aperture calculation model was established in three segments in accordance with the different fracture dips, and the influence factors affecting the fracture inverting inclination were analyzed using multi-fracture simulation images. Finally, the accuracy of the new model was verified with the simulated fracture images. The novel model for calculating fracture aperture is of great significance for processing and interpreting LWD resistivity imaging logging data.

A Methodology to Estimate Proximate and Gas Content Saturation with Lithological Classification in Coalbed Methane Reservoir, Bokaro Field, India

Well log analysis and production testing in coal are the initial requirements to judge the prospectivity of a coalbed methane (CBM) reservoir. The process of prospect identification through laboratory studies is accurate although it is time-consuming and expensive. Therefore, we developed a methodology to identify prospective coal seam by establishing multiple regression models of geophysical well log parameters vs. organic/inorganic contents from laboratory-tested core samples for one seam. The Langmuir’s equation and methane adsorption isotherm were used to estimation of gas and saturation content by developing a regression model from organic content. Gas and coal contents (ash, moisture, fixed carbon, and volatile matter) were obtained from the subsequent propagation of the established equations to other wells. Gas saturation increased with depth from 60 to 69%. Mapped seam thickness and gas content were in the ranges of 10.0–54.0 m and 6.1–28.2 cc/g, respectively. Overlaying of seam thickness and gas content identified the sweet spots in releasing potential future well locations. Errors within the permissible limit between the predicted and observed values indicate the gas estimation to be reliable. Another application for electro-facies classification was demonstrated by applying multi-resolution graph-based clustering architecture to capture texture parameters from histogram and auto-covariance function in resistivity image log. Determination of lithology by correlation of resistivity image and geophysical well log corroborated with the depositional environment having fining upward formational sequence. Thus, this study helps in estimating proximate components, gas content, and saturation with depth in coal seam for production optimization to better understand its implications on the dewatering and gas production periods in the Bokaro CBM reservoir situated in India.

Delineation of discontinuity using multi-channel seismic attributes: An implication for identifying fractures in gas hydrate sediments in offshore Mahanadi basin

Seismic attributes derived from post stack seismic data are important tools to delineate and characterize the subsurface geological features and discontinuities such as fracture, fault and many more. In this paper we have used two seismic profiles (MH-38A and MH-38B) of 2D multi-channel data along north-south direction with seafloor depth ranging from 1450 m to 2175 m in the deep offshore Mahanadi basin. The gas hydrate indicator, bottom simulating reflector (BSR) is easily identified but the discontinuities such as fractures are not observed directly from the seismic data. Therefore, we have computed the total four most suitable attributes which are reflection strength, instantaneous frequency, instantaneous bandwidth and instantaneous quality factor. We have compared the relative changes of the attribute values along the layers in the four derived attributes and hence, have recognized total three and seven discontinuities for MH-38A and MH-38B respectively, at near and away from the well location. Most faults are passing in NNW-SSE and N-S direction either in the gas hydrate layer or below BSR. These discontinuities are called as natural fractures which are validated by the fractures observed from resistivity image log at well site NGHP-01–19 and NGHP-01–09 for CDP ranges 1561 to 1681 in line MH-38A and 1357 to 1458 in line MH-38B respectively. The discontinuities in the gas hydrate sediments are useful for anisotropy study and to map the chimney system through which the free gas can flow from below BSR to up direction.

Azimuthal P-P prestack amplitudes in the presence of oil-filled aligned porosity (fracture porosity)

A 3D P-P high-fold full-azimuth full-offset reflection survey was acquired and processed to characterize a naturally fractured carbonate reservoir. The reservoir is a thick carbonate, which will flow commercial oil with a sufficient fracture network. Extensive calibration data include (1) a horizontal borehole’s resistivity image log, (2) the first 24 months cumulative oil produced, by stage, as known from chemical frac tracer data, (3) pre- and postfrac job instantaneous shut-in pressures, (4) microseismic, and (5) wireline log data. We used the cumulative oil production to document the spatially varying amount of aligned vertical porosity (aligned compliance or fracture porosity) connected to the stage borehole location. The stages of high oil production exhibited, for the fracture-perpendicular azimuth, the more positive amplitude variation with angle (AVA) gradients, and dimmer near-angle (6°–15° angles of incidence) amplitudes, compared to the fracture-parallel azimuth. The azimuthal variation of the AVA gradient fit the cos 2θ curve well, indicating the presence of one set of vertical aligned fractures dominating the azimuthal amplitude signature. In a similar fashion, the azimuthal variation of the mathematical intercept, physically the near-angle amplitudes, also fit the cos 2θ curve well. We have constructed crossplots of the azimuthal near-angle amplitude versus the AVA gradient on a bin-by-bin basis: we observed a straight line at bins with elevated oil production (elevated fracture density). A straight line crossplot of the (AVA gradient, mathematical intercept) is the signature of change of the (sensed) porosity, as long as the lithology and pore fluid are held constant. In accord with industry knowledge, we found that porosity affects the P impedance and thus the near-angle amplitudes: the aligned porosity yields azimuthal P impedance (measured at the 6°–15° angles of incidence). Legacy high-fold 3D P-P surveys rich in the 6°–20° angles of incidence should be considered for reprocessing and reinterpretation using these techniques.

Astronomically forced variations in multiresolution resistivity logs of lower Upper Cretaceous (Cenomanian-Coniacian) terrestrial formations from the Songliao Basin, northeastern China

Continuous and high-resolution well logs from the SK-2 east (SK-2e) borehole in the Songliao Basin provide an opportunity to understand the relationship between astronomically forced climate and electrical responses in terrestrial Cretaceous strata. Here, we present well logs, mud logs, and core measurement data of lacustrine deposits in the Upper Cretaceous Qingshankou Fm and of fluvial/deltaic deposits in the Quantou Fm. The multiresolution resistivity logs used for the cyclostratigraphic analysis include deep laterolog resistivity logs, resistivity image logs, and array induction logs. The results show that the resistivity of strata in Quantou Fm mainly reflects changes in clay content, and that resistivity logs can be used as paleoenvironmental and paleoclimate proxies in cyclostratigraphic studies of the Quantou Fm. For the Qingshankou Fm, formation resistivity is mainly affected by clay minerals, which contribute to the overall electrical conductivity, as well as nonconductive organic carbon. Increasing clay and organic carbon contents usually correspond to wetter and warmer climatic conditions. The phase relationship between resistivity logs and astronomical cyclicity depends on factors that control resistivity. Spectral analyses of lithological changes and resistivity logs indicate the presence of eccentricity, obliquity, and precession cycles throughout the Quantou Fm. This provides strong evidence that climate was the dominant control on cyclic sedimentation in the Quantou Fm. The floating astronomical timescale, which was established by calibrating extracted 405-kyr cycles from resistivity logs, reveals that the duration of Quantou Fm was approximately 5.5 Myr. Comparative analysis of power spectra reveals that the sensitivity of resistivity logs with specific resolutions to Milankovitch cycles with different frequencies is variable. Comprehensive analysis of multiresolution resistivity logs is a new method in cyclostratigraphy analysis that has the potential to be effective in detecting signals of Milankovitch cycles in terrestrial deposits.

An integrated approach for heterogeneity analysis of carbonate reservoirs by using image log based porosity distributions, NMR T2 curves, velocity deviation log and petrographic studies: A case study from the South Pars gas field, Persian Gulf Basin

Reservoir quality, pore types and heterogeneity study in carbonate reservoirs are considered as major concerns for choosing an economic reservoir in oil and gas industry. The optimized production rate and hydrocarbon recovery also needs a reservoir architecture evaluation. Therefore, having an accurate knowledge about the complexity of carbonate formations leads the hydrocarbon exploration and production projects to a more reliable achievement.In the current study, the Permo-Triassic aged carbonate formations of Dalan and Kangan in the world's largest not-associated gas field, South Pars, are selected as the target reservoirs. A comprehensive petrophysical evaluation in an appraisal well of the field resulted in interpretation of lithology and main reservoir quality parameters (porosity and permeability) using conventional and advanced magnetic resonance (NMR) logs. For more accurate understanding of lithology, rock fabric and fluid flows, heterogeneity is measured by applying Lorenz plot and coefficient of variation (CV) methods based on the petrophysical logs. In the next step, reservoir rock typing separated the productive and non-productive zones in different lithological intervals including K1, K2, K3, and k4. Finally, pore types and heterogeneities were investigated by porosity histograms received from formation resistivity image logs, porosity distributions of nuclear magnetic resonance, velocity deviation logs and thinsection studies. Histogram modalities and distributions of the image log bin porosities confirmed by NMR log, velocity deviation log and thin sections show that formation resistivity images are considerable tools for carbonate heterogeneity studies. The results of this study suggest that the paleoenvironmental conditions and diagenetic processes including dolomitization and dissolution controlled the porous media complexity in the K2 and K4 units, the major reservoir intervals of South Pars field.