Microresistivity Images Research Articles

Understanding Lateral Permeability Variations in Heterogeneous Carbonate Reservoirs Using Logging-While-Drilling NMR, Microresistivity Imaging, and Azimuthally Oriented Formation Testing

Understanding Lateral Permeability Variations in Heterogeneous Carbonate Reservoirs Using Logging-While-Drilling NMR, Microresistivity Imaging, and Azimuthally Oriented Formation Testing

Utilizing logs to identify complex lithology of tight marl reservoir in the Leikoupo Formation 32 Submember (T2l32) of the Sichuan Basin, China

Recent exploration has revealed that the Middle Triassic Leikoupo Formation 32 Submember (T2l32) in the Sichuan Basin contains unconventional marl reservoirs with significant natural gas potential. Due to limited cores, old wells, and conventional logs, however, the lithological understanding of T2l32 is incomplete and relies solely on inaccurate mud logs. This lack of lithological foundation challenges geology and petroleum research. To identify complex lithology, this paper presents a double-hierarchical workflow to identify seven types of lithology using logs. The first order distinguishes salt, anhydrite, and marl, while the second order further subdivides marl into anhydrite marl, argillaceous limestone, shaly limestone, and limy shale. Different rocks’ logging response characteristics are summarized based on quantity-limited cores and micro-resistivity imaging logs. Lithological identification of 2D and 3D plots is established using sensitive GR, DEN, and RT. Corresponding identification standards are built in two hierarchies. According to these standards, the lithology of T2l32 is identified in a total of 119 wells. Finally, the lithological characteristics of vertical, horizontal, and plane are discussed in the research area. The research results may aid in comprehending the entire lithological characteristics of the complex marl reservoir in T2l32 of the Sichuan Basin. It would help the exploration potential of petroleum systems in turn.

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.

A deep-learning approach for dynamic region merging applied to feature extraction from borehole microresistivity images

The primary purpose of processing borehole resistivity images is to identify and extract high (or low) resistivity anomalous areas, which are associated with resistive fractures and dissolved pores. To improve the accuracy and applicability of these models, a new intelligent method combining dynamic-region merging (DRM) with a deep-learning network (U-net) is proposed. The superpixel method, also referred to as linear spectral clustering (LSC), was applied to segment fractures and dissolved pores that are represented by a resistivity contrast in the original resistivity images. The sequential probability ratio test technique was then used to implement the DRM procedure to group many oversegmented small regions. A convolutional neural network (U-net architecture) model was used to automatically identify geologic features with various scales. The trained neural network was then used to identify the segmented resistivity images that had been processed by the DRM. The results showed that the superpixel algorithm and U-net combination significantly improved the accuracy of the classification and identification of fractures and dissolved pores in different test data sets. Moreover, it solved the problem of few-shot learning, enabling a pretrained model to generalize over new data categories.

Vaca Muerta: Improved Fracture Width Distribution and Classification of Natural Fracture Widths Based on Outcrops, Cores, and Microresistivity Images Data

Summary Natural fractures in Vaca Muerta are very complex, such that their fracture width distributions cannot be analyzed simply by considering normal, log-normal, or log-log distributions. Natural fractures are commonly classified as macrofractures or microfractures; however, no consistent fracture width is attached to those fractures. In this study, two new approaches are proposed; an improved fracture width distribution and a classification for natural fractures that encompasses all physical widths found in petroleum reservoirs. The method developed in this study first evaluates the distribution of natural fracture widths from outcrops, cores, and microresistivity images of Vaca Muerta shale. An improved fracture width distribution is established through a variable shape distribution (VSD). The model provides a good fit, even if the shape of the distribution deviates from generally accepted distributions. This improves the accuracy of fracture width and intensity prediction, which is useful in generating synthetic production logging tools (PLTs) to estimate productivity from fractured intervals. Subsequently, a consistent classification for natural fractures is introduced to cover all fracture widths found in petroleum reservoirs. Results indicate that fracture widths in Vaca Muerta shale range between 0.0003 mm and 7 mm for outcrops, 0.0003 mm and 2 mm for cores, and 0.01 mm and 2 mm for microresistivity images. The VSD model provides a good fit of fracture widths from the three sources, without truncating any of the data. Truncation of data is usually required when using generally accepted distributions. With this improved distribution, size pattern extrapolation can be performed with greater accuracy. The physical widths can also be translated into hydraulic apertures to generate theoretical PLT. This is useful for estimating relative petroleum production potential from each fractured interval and for identifying future refracturing zones. Additionally, the study gives origin to a consistent classification of fracture widths that has application in Vaca Muerta and other oil and gas reservoirs. Five subclasses are introduced, which are megafractures (> 10 mm), macrofractures (1–10 mm), mesofractures (0.1–1 mm), microfractures (0.01–0.1 mm), and nanofractures (<0.01 mm). A careful review of the literature indicates that there is ambivalence as it is hard to find a clear and precise terminology that encompasses the entire range of fracture widths. The proposed classification eliminates that difficulty. In this paper, for the first time, a consistent fracture width classification is developed that encompasses the whole spectrum of widths found in petroleum reservoirs. It has wide application in Vaca Muerta, where widths, derived from outcrops, cores, and microresistivity image data are matched with a VSD model. Furthermore, the proposed classification can be used in other oil and gas reservoirs, thus eliminating the fracture width ambivalence found many times in the geoscience and petroleum engineering literature.

Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag

Abstract Geological outcrop is usually the first-hand evidence to cognize the probable geometry, lateral extent, and other properties of conglomeratic channels. If there is no exposure of formation, however, it is hard to recognize elaborate architectural elements just based on poor coverage cores and low-resolution conventional logs vertically. In order to understand vertical distributions of conglomeratic channel architectural elements in the Lower Triassic Baikouquan Formation in southern slope of Mahu Sag, ultrahigh-resolution microresistivity image logs are used to recognize elements under the concept of conglomeratic channel architectures in this article. The microresistivity images were calibrated with cores in different grain-sizes and sedimentary structures. According to microresistivity image fabric, subaerial channel-fills consisted of granules to cobbles; subaqueous channel-fills were composed of well-sorted sandstones to granules. Subaerial channel-margins comprised sandstones to pebbles; subaqueous channel-margins were granules and sandstones. Bypass drapes, deposited from the tail of bypassing flows, were commonly eroded by overlying channel-fills. Convergent drapes, accumulated on top surfaces of channel-margins, preserved well without reworking. Overbank drapes, formed outside the channel, were thick-bedded. Combined with dipmeter pattern of microresistivity image log, six channel story sets were recognized depending on abrupt changes in dip azimuths. In each channel story set, per unit thickness of mud drapes ranged from 0.1 to 0.6 m. Mud drapes in channel story set 1 could be correlated horizontally. In channel story sets 3, 4, and 5, thickness of mud drapes ranged from 0.05 to 2.59 m and averaged at 0.59 m, and channel conglomerates and mud drapes alternated frequently. These understandings would provide important insights on the production potential of petroleum.

An Improved GAN-Based Image Restoration Method for Imaging Logging Images

An improved GAN-based imaging logging image restoration method is presented in this paper for solving the problem of partially missing micro-resistivity imaging logging images. The method uses FCN as the generative network infrastructure and adds a depth-separable convolutional residual block to learn and retain more effective pixel and semantic information; an Inception module is added to increase the multi-scale perceptual field of the network and reduce the number of parameters in the network; and a multi-scale feature extraction module and a spatial attention residual block are added to combine the channel attention. The multi-scale module adds a multi-scale feature extraction module and a spatial attention residual block, which combine the channel attention mechanism and the residual block to achieve multi-scale feature extraction. The global discriminative network and the local discriminative network are designed to gradually improve the content and semantic structure coherence between the restored parts and the whole image by playing off each other and the generative network. According to the experimental results, the average structural similarity measure of the five sets of imaged logging images with different sizes of missing regions in the test set is 0.903, which is an improvement of about 0.3 compared with other similar methods. It is shown that the method in this thesis can be used for the restoration of micro-resistivity imaging log images with good improvement in semantic structural coherence and texture details, thus providing a new deep learning method to ensure the smooth advancement of the subsequent interpretation of micro-resistivity imaging log images.

Integrated borehole image and rotary sidewall core data to support infrastructure-led appraisal: Capercaillie Field, Central North Sea

Abstract Quad 29 in the Central North Sea is a focus for bp, with a strategy to identify remaining hydrocarbon accumulations to tieback to existing infrastructure. Capercaillie was appraised in 2017 with well 29/04e-5 and sidetrack 29/04e-5z. A gas cap and thin oil rim were intersected within the siliciclastic Paleocene–Eocene Sele Formation. The reservoir sandstones were deposited in a deep marine setting by sediment-gravity flow processes. Within the context of a relatively detailed knowledge of the surrounding area, the reservoir data-operational risk-cost balance was addressed through acquisition of a full wireline logging suite, pressure data, latest-generation microresistivity images and targeted large-volume rotary sidewall cores (RSWCs), with the latter two favoured over whole core. Mature deepwater descriptive schemes were applied at the sidewall core (lithotype), bed (borehole image facies) and bed-stack (depositional package) scales. This hierarchical approach provided robust sedimentological data to underpin higher-order depositional models, which together were used as a framework to (1) constrain the reservoirs’ mineralogical and textural attributes, (2) establish the main controls on rock quality and (3) explain the resulting variations in porosity and permeability. This study demonstrates that the careful integration of data derived from the latest borehole imaging tools and large-volume RSWCs can be a successful means of characterizing reservoirs from a sedimentological, reservoir quality and reservoir architecture perspective in mature basins. Similar approaches to geological reservoir characterization in such settings are likely to be a common cornerstone of cost-effective development during the energy transition.

Application of artificial neural network models and random forest algorithm for estimation of fracture intensity from petrophysical data

Natural fractures play an essential role in the characterization and modeling of hydrocarbon reservoirs. Modeling fractured reservoirs requires an understanding of fracture characteristics. Fractured zones can be detected by using seismic data, petrophysical logs, well tests, drilling mud loss history and core description. In this study, the feed-forward neural networks (FFNN), cascade feed forward neural networks (CFFN) and random forests (RF) were used to determine fracture density from petrophysical logs. The model performance was assessed using statistical measures including the root mean squared error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Kling Gupta efficiency (KGE) and Willmott’s index (WI). Conventional good logs and full-bore micro-resistivity imaging data were available from three drilled wells of the Mozduran reservoir, Khangiran gas field. According to the findings of this research, the FFNN model showed a higher KGE and WI, and a higher correlation coefficient (R2) compared to the CFNN model. The CFNN model outperformed the FFNN model with lower neurons. The models' performance was also improved by increasing the number of neurons in the hidden layers from 8 to 35. The findings of this study demonstrate that the measured and FFNN calculated fracture intensity is in excellent agreement with image log results showing a correlation coefficient of 92%. The RF algorithm showed higher stability and robustness in predicting fracture intensity with a correlation coefficient of 93%. The results of this study can successfully be used as an aid in a more successful reservoir dynamic modeling and production data analysis.

Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the local texture details of complex well logging images, two discriminators (global and local) are introduced to ensure the global and local consistency of the image; the local discriminator can better focus on the texture features of the image to provide better texture details. Secondly, in response to the problem of feature loss caused by max pooling in U-Net during down-sampling, the convolution, with a stride of two, is used to reduce dimensionality while also enhancing the descriptive ability of the network. Dilated convolution is also used to replace ordinary convolution, and multiscale contextual information is captured by setting different dilation rates. Finally, we introduce residual blocks on the U-Net network in order to address the degradation problem caused by the increase in network depth, thus improving the quality of the filled logging images. The experiment demonstrates that, in contrast to the majority of existing filling algorithms, the proposed method attains superior outcomes when dealing with the images of intricate lithology.

Characterization of reservoir properties and pore structure based on micro-resistivity imaging logging: Porosity spectrum, permeability spectrum, and equivalent capillary pressure curve

According to the capillary theory, an equivalent capillary model of micro-resistivity imaging logging was built. On this basis, the theoretical models of porosity spectrum (ϕi), permeability spectrum (Ki) and equivalent capillary pressure curve (pci) were established to reflect the reservoir heterogeneity. To promote the application of the theoretical models, the Archie's equation was introduced to establish a general model for quantitatively characterizing ϕi, Ki, and pci. Compared with the existing models, it is shown that: (1) the existing porosity spectrum model is the same as the general equation of ϕi; (2) the Ki model can display the permeability spectrum as compared with Purcell's permeability model; (3) the pci model is constructed on a theoretical basis and avoids the limitations of existing models that are built only based on the component of porosity spectrum, as compared with the empirical model of capillary pressure curve. The application in the Permian Maokou Formation of Well TSX in the Central Sichuan paleo-uplift shows that the ϕi, Ki, and pci models can be effectively applied to the identification of reservoir types, calculation of reservoir properties and pore structure parameters, and evaluation of reservoir heterogeneity.

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.

The effect of fracturing on permeability in carbonate reservoir rocks

Hydrocarbon flow rates from tight carbonate reservoirs often depend on the distribution and properties of fractures throughout the reservoir interval. Hence, quantification of fracture parameters can contribute to understanding of reservoir characteristics and reservoir potential. In this paper, natural fractures in tight carbonate reservoir rocks of the Upper Cretaceous Shiranish Formation in the Taq Taq field (Kurdistan Region of North Iraq) were examined primarily using core samples, XRMI micro-resistivity image logs and conventional wireline logs, with additional dynamic data from well tests and mud logs. The Shiranish Formation is composed of a variety of limestones and argillaceous limestones with arithmetic mean matrix porosity and geometric mean permeability values of 1.64 ± 0.37% and 0.0223 ± 0.59 mD, respectively. However, fracturing has enhanced the permeability by up to four orders of magnitude above the maximum matrix permeability (1.79 ± 0.59 mD). Hydrocarbons are produced entirely from the fracture network in these rock types in the Taq Taq reservoir. Observed open and partially-open fractures have NE-SW orientations; these are classified as macro-fractures with average aperture of 0.17 ± 0.18 mm, arithmetic mean height of 11.70 ± 45.40 mm and arithmetic mean length of 26.7 ± 32.32 cm, where the uncertainties are standard deviations. Fracture aperture size and fracture height control fracture permeability and fluid flow. However, while fracture frequency has no effect on permeability, fracture distance distribution influences flow rates and hydrocarbon production. The dominant NE-SW trending fractures are more likely to remain open within the NE-SW oriented present day stress field at Taq Taq. Furthermore, the effect of overburden stress on the permeability and production rate was examined in laboratory tests on core plugs by increasing the confining stress during permeability measurements using constant flow rates. It was found that the magnitude of fracture permeability was reduced by 70% by applying 4000 psi of confining stress. In the reservoir, this effect would be exacerbated by reduced reservoir fluid pressures as a function of production. However, fractures with apertures greater than 0.10 mm are preserved as productive fractures even at high confining pressures and implying that hydraulic fracturing could open existing fractures significantly, enhancing production. This last observation implies that the use of hydraulic fracturing in tight carbonate reservoirs with natural fracturing is likely to be commercially viable.

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.

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).

A Universal Multi-Frequency Micro-Resistivity Array Imaging Method for Subsurface Sensing

In this paper, a universal multi–frequency micro-resistivity array imaging (UMMAI) system for subsurface sensing is developed and verified. Different from conventional micro-resistivity imaging equipments, UMMAI is capable to provide high-resolution fullbore formation images in multiple logging environments including an oil-based mud scene, water-based mud scene and water-oil mixed mud scene, owning to the large dynamic range and good linearity of transceivers. With the advantage of diversity in excitation signal frequency, UMMAI presents abundant amplitude–frequency characteristics response images and phase–frequency characteristics response images of subsurface formations at the same time, which is beneficial to multi–frequency image fusion in the future. The fullbore imaging ability of UMMAI is evaluated in three different field tests, and the results show that UMMAI can give satisfactory credible formation images with high resolution, which is suitable for subsurface formation discrimination and useful for reservoir identification.

Lithologic classification of pyroclastic rocks: A case study for the third member of the Huoshiling Formation, Dehui fault depression, Songliao Basin, NE China

With the ongoing discovery of volcanic oil and gas reservoirs, pyroclastic rocks have begun to attract widespread attention from both the academic and industrial sectors. Because there is more information about the cementation mode and the grain size in the Chinese classification system for pyroclastic rocks than there is in the system used throughout the rest of the world, we use the Chinese classification system to investigate the lithologies of the pyroclastic rocks in our study area. The three cementation modes dictate the three classes of pyroclastic rock: pyroclastic lava, normal pyroclastic rock, and sedimentary pyroclastic rock. Within these three modes, pyroclastic rocks are further classified by their clastic material grain size into agglomerate, breccia, and tuff. If the pyroclastic material is moved by aeolian transport, it tends to follow the positive sedimentation cycle sequence. In this study, we classify the pyroclastic rocks from the third member of the Huoshiling Formation (Fm) in the Dehui fault depression in the southern Songliao Basin. Draw the cross plots of pyroclastic lava, normal pyroclastic rock and sedimentary pyroclastic rock in the study area. Based on core date, fullbore micro-resistivity images (FMIs), and thin section analyses, we determine the mineral composition, particle size, and cementation mode of breccia bearing tuff lava, tuff lava, breccia bearing welded tuff, volcanic breccia, breccia bearing tuff, tuff, sedimentary volcanic breccia, and sedimentary tuff. Our results indicate that the decision tree method does not require all of the input curves to identify a given sample's lithology. We also found that adding more data to a support vector machine (SVM) does not necessarily translate into more accurate classification results. This classification is undertaken using two different techniques: a combination of elemental capture spectroscopy (ECS) cross plots and FMI and a SVM learning algorithm with information derived from node information in a decision tree. Ultimately, the results produced by the SVM algorithm are more accurate (recognition accuracy of 98.6%) than those generated by the ECS/FMI approach. Furthermore, the SVM algorithm requires less data and operates very efficiently. Using data from multiple wells allows for better and more rapid identification of the lithology type in the target layer; large observational or training datasets provide a strong foundation for further reservoir evaluation.

Fine Logging Evaluation of Fractures in Continental Tight Oil Sandstone Reservoirs of Yanchang Formation in the Ordos Basin

Fractures are the main seepage channels in tight reservoirs, and they affect the distribution of high-quality reservoirs and the enrichment of hydrocarbons. Fine logging identification of fractures in the strongly heterogeneous continental tight oil reservoirs of the Upper Triassic Yanchang Formation in the Ordos Basin is a hot and difficult point in the field of petroleum geology. In this paper, taking the Yanchang Formation as an example, a fractal model was constructed to identify fractures in tight oil reservoirs using a large number of cores, conventional and micro-resistivity imaging logging data. The results show that high-angle, vertical and bedding fractures are mainly developed in the Yanchang Formation tight sandstones. There is a negative correlation between sand body thickness and fracture development degree. The fracture sensitivity parameters were used to construct a coupled fractal fracture index. The fractal model incorporates logging information from natural gamma, acoustic wave time difference, rock density, and shallow lateral resistivity. In addition, the constructed fracture fractal index realizes the functions of multi-conventional logging information fusion, which can effectively identify fracture development segments in sandstone. According to the statistics, the fracture identification rate is 83.3%. The study also found that with the increase of sandstone brittleness index, the fracture index has a “S” shape increasing trend. Therefore, the content of brittle mineral components in sandstone is an important factor affecting the development degree of natural fractures, and fractures are more likely to occur in high brittle, thin sand bodies. The highly brittle framework minerals have strong stress-supporting capacity, which can keep fractures open by resisting high overlying loads.

Petrophysical Facies and Inferences on Permeability at Brothers Volcano, Kermadec Arc, Using Downhole Images and Petrophysical Data

Abstract Downhole data and cores collected during International Ocean Discovery Program (IODP) Expedition 376 at Brothers volcano, Kermadec arc, provide unprecedented, in situ views of volcanic facies and fluid pathways in an actively forming volcanogenic massive sulfide (VMS) ore deposit. Brothers volcano is a submarine caldera with extensive sea-floor hydrothermal alteration. Downhole data were collected in two holes: Hole U1530A at the NW Caldera and Hole U1528D at the Upper Cone. Textural analysis of microresistivity images in Hole U1530A provides a continuous image facies record that greatly improves findings based upon sporadic and partial (18%) core recovery. Between 90 and 214 meters below sea floor (mbsf), the heterogeneous image facies with local pattern variations is consistent with the volcaniclastic facies interpreted from cores. Between 232 and 445 mbsf, a volcanic facies was not recognizable in cores because of overprinting alteration, apart from five intervals of coherent lava flows that were less altered. Based on the fairly constant petrophysical data, Vp-porosity relationship, and presence of five to six coherent image facies intervals on the microresistivity image, we propose that the apparent volcaniclastic textures observed on cores and microresistivity images beneath 232 mbsf are dominantly lava flows. The change from volcaniclastic to dominant lava flow facies occurs over a transition zone (214–232 mbsf) where all petrophysical properties gradually change. In Hole U1528D, cores and petrophysical data show a similar transition from deep coherent lava flows to shallower, largely volcaniclastic sequences at ~270 mbsf. Down to 232 mbsf in Hole U1530A and 360 mbsf in Hole U1528D, the overall first-order downward decrease in porosity is interpreted to be caused by compaction and increased alteration intensity. Volcanic facies and fractures exert a second-order local control on petrophysical properties. Beneath 232 mbsf in Hole U1530A, the prolonged hydrothermal activity is inferred to have diminished local petrophysical property variations within the proposed lava flow-dominated rock package. High downhole fluid temperatures in Hole U1528D contrast with the moderate temperatures in Hole U1530A. Permeable zones show a mix of structural (inferred fault in Hole U1530A) and lithological controls in both holes. Some low-permeability layers and/or lithological interfaces possibly focus fluids laterally in higher-permeability layers, which may act as a trap for metal-rich fluids to form stratabound massive sulfides and deposits. Matrix is likely too low in permeability to conduct fluids but provides perfect conditions for the storage of supersaline brines. In Hole U1530A, located near active vents at the sea floor, the relatively low fluid temperature and the alteration overprint of moderate temperature demonstrate the high spatial and temporal variations at Brothers volcano. The implications of the new stratigraphy and controls on permeability proposed here for Brothers volcano include a better understanding of the following: (1) submarine volcanic eruption sequences, (2) permeability in active submarine volcanoes, and (3) the formation of volcanogenic massive sulfide deposits on (and near) the sea floor.

Reviewing the correlation potential of Spectral Gamma Ray: a case study in Ordovician glacial environments in the Murzuq basin, SW Libya

The deposits of the Late Ordovician glaciation have been the focus of several studies in recent years reflecting significant hydrocarbon reservoir potential in different areas of the world, most notably in Libya and Algeria but also in Saudi Arabia and Bolivia. The study area for this paper is located within the Murzuq Basin of SW Libya. This study aims to evaluate the potential of Spectral Gamma Ray, integrated with other datasets (geochemical data, core descriptions and Micro-resistivity Images) to enhance correlation in glacial reservoirs where biostratigraphy cannot be applied and conventional logs lack resolution or consistency. The methodology chosen correlates the SGR logs and elemental ratios in wells with geochemical and sedimentological data to understand possible relationships between Th, U, K and depositional environments. Subsequently, this tool is used to analyse wells for which no detailed sedimentological data is available to understand the spatial configuration of depositional environments, correlation and thus, reservoir potential across the study area. The study demonstrates how the use of the SGR ratios can be useful as a correlation tool, how Th/U varies with distance from the sediment source area; but also highlights that whilst SGR ratios are not able to consistently discriminate between individual facies associations, given the large standard deviations, they are nonetheless very useful as general predictors of broad depositional. Additionally, a review of possible sources for the Th and U is provided together with some conclusions aimed at improving the understanding of the architecture of the glacial sediments.