Abstract This study conducts a statistical analysis of global natural hydrogen occurrences, revealing critical patterns and proposing future exploration strategies. By updating previous databases (including >10% hydrogen occurrences), we compile 411 data points. These are classified and compared using databases related to tectonic, geophysical, surface, and mineral resource components. Anomaly detection is applied to identify local and regional patterns through location property analysis and the modified z-score method. The analysis highlights the strengths and limitations of current data sets and offers recommendations for future research. This study enhances our understanding of the distribution of high hydrogen occurrences, highlighting significant patterns primarily associated with proximity to geologic province boundaries, active faults, mineral commodities, and mineralizations. In addition, it identifies anomalies linked to geophysical and surface components and evaluates the potential of specific databases as practical exploration tools.

Abstract Machine learning is a powerful data-driven technology for regression and classification tasks in numerous branches of science and technology. With probabilistic machine learning, we can approximate the probability density function of a given digital data set, unlocking the sampling of new and realistic data. We implement probabilistic machine learning as a statistical-based data augmentation tool to effectively improve the performance of a classifier on scarce data sets. Limited data are the norm in most practical situations, as is the case with core samples retrieved from unconventional oil and gas reservoirs. Obtaining and properly labeling rock sample images is a manual and time-consuming task. This leads to scarce data sets that make the lithologic classification of rock samples challenging. We illustrate the technology on images taken from well cores from the Argentinean Neuquén and Austral Basins, where by introducing generated samples in the training stage of a machine-learning classifier, we achieve a 5% higher accuracy with respect to the baseline obtained using the same number of samples from the original data set. We reach these results by proposing a lightweight workflow that can be run on very limited hardware. Our workflow is an alternative to classical data augmentation strategies for dealing with incomplete information, such as geometric transformations.

Abstract Porosity is one of the most important properties for evaluating hydrocarbon reservoirs. There is a strong nonlinearity between seismic data and rock-physical properties. Machine-learning methods possess the powerful ability to capture complex nonlinear relationships between these two parameters, holding significant potential for porosity prediction in tight sandstone reservoirs. In this study, we develop a seismic facies-controlled porosity prediction method based on the extreme gradient boosting algorithm. Through the seismic meme inversion method, we obtain high-resolution P-impedance data. In the inversion process, lateral variations of seismic waveforms were used instead of the variogram function. We create labels for model training using porosity curves and borehole side P-impedance data, applying them to the extreme gradient boosting regressor. To demonstrate its feasibility, we apply the model to a real 3D case from an oil field in the Songliao Basin. Our application results demonstrate that this method can provide porosity predictions for tight sandstone reservoirs, maintaining consistency with seismic waveforms and adhering to seismic facies control patterns. The method uses only acoustic traveltime, density, gamma rays, spontaneous potential well-log data, and poststack 3D seismic data as inputs, allowing for quick porosity predictions in the field. Compared with the seismic facies-controlled inversion combined with support vector machine and multiLayer perceptron methods, the method developed in this study provides more reliable porosity predictions, offering insights and references for the exploration and development of tight sandstone reservoirs.

Abstract The Cane Creek reservoir within the Paradox Basin in southeast Utah is an unconventional oil play that poses substantial geomechanical and hydrological challenges. With hundreds of millions of oil barrels in place, this resource is a viable target, but a greater understanding of the subsurface geomechanics is required for more reliable and consistent production. We analyze a previous hydraulic stimulation of the State 16-2 LN-CC well, located in the northern part of the basin, to produce recommendations for how future stimulations might be improved. Specifically, we look at three components of the completed stimulation: spatial correlations of various well logs to identify regions favorable for retaining hydrocarbons within natural fractures, analysis of pressure-time data from the hydraulic fracturing stages during well stimulation to assess the complexity of the fracture networks, and evaluation of poroelastic effects from reservoir depletion to assess the potential for future restimulation. Results of our analysis provide quantitative insight about the State 16-2 LN-CC stimulation and yield specific recommendations. The most important recommendation is to stimulate regions with geomechanically stable fractures due to their ability to retain hydrocarbons. Restimulation, if needed, may be more effective if completed after significant depletion due to poroelastic stress changes. Although the direction of maximum horizontal stress is a critical factor, it might be less crucial if restimulation is conducted after considerable reservoir depletion.

Abstract 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.

PreviousNext No AccessInterpretationJust-Accepted ArticlesIntroduction to special section: Borehole image data applications in reservoir characterization ? Case studies and updates on new developmentsAuthors: Bingjian LiVsevolod EgorovRicardo PeronaDavid HaddadKaty SementelliChicheng XuChrystianto MardiBingjian LiBlackriver Geoscience LLC, Houston, Texas, USA. E-mail: [email protected]., Vsevolod EgorovGeoExpera, Houston, Texas, USA. E-mail: [email protected]., Ricardo PeronaRepsol, Woodlands, Texas, USA. E-mail: [email protected]., David HaddadApache Corporation, Houston, Texas, USA. E-mail: [email protected]., Katy SementelliWoodside Energy, Houston, Texas, USA. E-mail: [email protected]., Chicheng XuAramco Americas Company, Houston, Texas, USA. E-mail: [email protected]., and Chrystianto MardiBPX Energy, Houston, Texas, USA. E-mail: [email protected].https://doi.org/10.1190/int-2024-0311-spseintro.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InReddit FiguresReferencesRelatedDetails Just-Accepted ArticlesPages: 1-44ISSN (print):2324-8858 ISSN (online):2324-8866 publication data© 2024 Society of Exploration Geophysicists and American Association of Petroleum GeologistsPublisher:Society of Exploration GeophysicistsAmerican Association of Petroleum Geologists HistoryPublished Online: 15 Mar 2024 CITATION INFORMATION BingjianLi, VsevolodEgorov, RicardoPerona, DavidHaddad, KatySementelli, ChichengXu, and ChrystiantoMardi, (), "Introduction to special section: Borehole image data applications in reservoir characterization ? Case studies and updates on new developments," Interpretation 0: 1-2. https://doi.org/10.1190/int-2024-0311-spseintro.1 Plain-Language Summary PDF Download Metrics Loading ...

Abstract For seismic wave propagation in media with horizontal transverse isotropy (HTI), kinematic and dynamic attributes are anisotropic. P waves propagate slower in perpendicular to fracture azimuth than parallel to it, whereas reflection strength and frequency vary with azimuth. The azimuthal effects on reflection coefficients and seismic wave velocities in an HTI medium are quantitatively analyzed. The resulting theory-based and real-world azimuth gathers demonstrate the anisotropic nature of such media. The fitting of the ellipse is used to quantitatively predict the direction and strength of anisotropy in the study area, which is consistent with that obtained by the S-wave splitting prediction method. In wide-azimuth data processing, measures are taken to eliminate the influence of azimuthal anisotropy, and the coherent spectrum pickup method is used to accurately calculate the azimuthal velocity of underground HTI media and perform azimuthal anisotropy correction processing, which eliminates the time difference between fast and slow wave caused by azimuthal anisotropy. The achieved results provide support for subsequent high-resolution imaging.

Abstract The Mula River Basin is in an active tectonic region of the Kirthar fold fault belt in the Western Himalayas. The presence of numerous major faults like Chaman Fault, Kirthar Frontal Fault, and Bannh Fault near the study suggests complex tectonic processes in the region. The seismic record of the study area also indicates that this area is tectonically active. This makes the study area an ideal site to measure tectonic activity through geomorphic indices like hypsometric integrals (HI). For tectonic analysis of the Mula River Basin, we divided the study area into 309 subbasins. The results obtained from the HI calculations for the subbasins led us to the classification of the study area into three classes, i.e., class 1 (0.51–0.78), class 2 (0.37–0.50), and class 3 (<0.37), where class 1 is for the highest tectonic activity, class 2 responds to moderate, and class 3 is for the lowest tectonic activity. We calculated the hypsometric curves to understand the geomorphological cycle of the Mula River Basin. We quantified the subbasins of the Mula River Basin inside the highly active tectonic zone of Kirthar fold and fault zone as per their tectonic activity and found that a major portion of the study area indicates low tectonic activity (44.33%), medium tectonically active, and high tectonically active subbasins are 37.86% and 17.79%, respectively. These findings are supported by the presence of high-relief areas and known faults in the study area.

Abstract The distribution and characteristics of the Moho reflections (M-reflections) in oceanic basins record the seafloor spreading process and provide crucial insights into the formation and evolution of the oceanic basins. Approximately 20,000 km of multichannel seismic reflection sections are used to characterize the M-reflections and the crustal structures in the South China Sea oceanic subbasins. The identified M-reflections are mainly distributed in the northwest subbasin, the east subbasin, and the northern and southern flanks of the southwest subbasin (SWSB). They exhibit a banded distribution pattern in two-way traveltime along the north–south direction, with shallower depths observed in the central region and gradually deepening toward the northern and southern sides. In addition, the M-reflection amplitudes generally weaken, and the reflection events become less continuous as the igneous oceanic crust thins along with the seafloor spreading prior to the second ridge jump event. Ultimately, the M-reflection becomes indistinguishable in proximity to fossil spreading centers. These observations suggest a decreasing magma supply toward the cessation of the spreading. Despite the significance of Moho interface imaging at the crust-mantle boundary, the distinct M-reflections are absent in the SWSB and the vicinity of the Zhenbei-Huangyan Seamounts Chain, which are attributed to several factors, especially the vague nature of Moho interfaces.