Subsurface Reservoir Characterization Research Articles

A CNN-accelerated workflow for stochastic seismic property estimation

As one of the major tools in resolving the non-uniqueness challenge in subsurface interpretation and reservoir characterization, stochastic inversion from post- and pre-stack seismic data remains challenging, which not only requires heavy computational resources but also relies on intensive manual supervision. Inspired by the recent advances in deep learning particularly convolutional neural networks (CNNs) for interdisciplinary data integration, this study proposes a deep learning workflow that enables stochastic property estimation by efficiently integrating seismic images with sparse wells. It starts with sampling a set of property prior models (PPMs) from densely-measured properties at well locations and corrupting local seismic patterns with Gaussian noise. The core idea is to train a structure-guided CNN by mapping the contaminated seismic with the sampled PPMs while enforcing structural consistency to avoid overfitting in the presence of sparse wells. Finally, the baseline and uncertainty of target properties are estimated by running multiple realizations of the trained CNN. As demonstrated by three examples, with minimum efforts of CNN architecture customization according to data availability, the proposed workflow can accommodate various use cases, including rock acoustic/elastic property estimation from 3D post-/angle-stack seismic and soil geotechnical properties from 2D ultra-high-resolution seismic. In all examples, the machine predictions match seismic patterns well and are of high lateral consistency.

The DQ Trace Method for Quantitative Seismic Interpretation: Theory and Application to a Synthetic AVO Data Catalog

The Distance and Quadrant (DQ) trace is a novel seismic attribute that enhances the detection and visualization of hydrocarbons in seismic data. Generated through a combination of partial near and far stack traces convolved with phase shift filters, the DQ trace offers several advantages over traditional AVO attributes and inversion methods. This study presents the DQ trace generation process, which involves quadrant optimization and the application of a modified AVO two-layer model equation. The method is computationally efficient and does not require a priori knowledge of petrophysics, basin geology, or wavelets. Concurrent phase angle analysis is used to compare DQ traces with porosity logs, demonstrating a strong correlation between the two. Synthetic models for different AVO classes are employed to evaluate the performance of DQ traces in cross-section displays. The results show that DQ traces provide enhanced clarity and delineation of gas sands, wet sands, and thin beds, as well as improved visualization of tuning effects and fluid contacts compared to conventional seismic displays. DQ histogram analysis reveals systematic trends across AVO classes and allows for the separation of gas sands, wet sands, and shales. The DQ trace is a robust tool for porosity estimation and has potential applications in various seismic processes and basin-scale mapping. This study highlights the implications of the DQ trace for fluid detection and seismic interpretation, offering a new methodology in subsurface reservoir characterization.

Synthesizing a realistic seismic training data set incorporating prior geologic patterns and seismic imaging features for supervised convolutional neural network-based intracratonic strike-slip fault detection

Intracratonic strike-slip faults play a vital role in hydrocarbon migration and accumulation, making their accurate detection crucial for subsurface structure interpretation and reservoir characterization. Although numerous approaches have been developed for automatic fault interpretation, they are still unable to fully recognize intracratonic strike-slip faults with subtle reflection variations in seismic images. We develop a supervised convolutional neural network (CNN) to automatically and precisely delineate these faults in 3D seismic images. The primary obstacle in using a CNN for this task is the lack of diverse seismic training samples and corresponding high-quality labels. To address this, we develop an efficient two-step workflow for automatically generating a tremendous amount of 3D seismic training sample pairs: (1) guided by the prior geologic structure patterns of the study area, we use parameterized vector fields to simulate various velocity models with accurate fault labels incorporating realistic intracratonic strike-slip faulting features, such as the transtensional, transpressional, and strike-slip translational types and (2) following seismic imaging laws, we synthesize seismic training samples from simulated velocity models through the point-spread function-based convolution method. One synthetic and two field examples demonstrate that the CNN trained with this seismic data set containing prior geologic patterns and seismic imaging features outperforms the conventional fault detection approaches in accurately and continuously delineating intracratonic strike-slip faults.

A machine-learning workflow to integrate high-resolution core-based facies into basin-scale stratigraphic models for the Wolfcamp and Third Bone Spring Sand, Delaware Basin

The characterization of subsurface reservoirs that are dominated by mudrock facies is hindered by the inherent heterogeneity and high degree of spatial variability typical of mudrock depositional systems. Subsurface reservoir properties that include porosity and permeability, fluid saturations, stratigraphic thicknesses of reservoir units, and source rock potential are ultimately controlled by the spatial distribution of sedimentary rock facies, which supports efforts to improve subsurface characterization workflows. Although core-based data provide direct measurements of rock attributes that are used to inform static reservoir models, capturing high-resolution core-based rock facies and downscaling these observations to tie to lower-resolution wireline logs remains a challenge. The effort to integrate core-based facies to reservoir-scale models is especially difficult when trying to capture thin-bedded heterogeneity that is common to mudrock systems. Herein, a workflow is developed and applied to visualize and integrate multivariate and spatially complex core-based data sets with wireline logs. Formation-specific core-based chemofacies training data sets are developed by integrating core descriptions with chemofacies clusters developed from high-resolution X-ray fluorescence core scanning. Core-based rock attribute data (e.g., XRD mineralogy, total porosity, and total organic matter content) are used to describe the chemofacies, providing a means to upscale low-resolution rock attribute measurements to high-resolution core-based chemofacies. Supervised core-based chemofacies training data sets are then used with neural network multiclass classification machine-learning tools to train triple combo wireline logs (gamma ray, deep resistivity, bulk density, and neutron porosity) to predict rock facies from wireline logs, providing a new approach to apply core-based facies classifications to wireline log studies. A basin-scale case study that applies this workflow is described for the Third Bone Spring Sand and units of the Wolfcamp Formation in the Delaware Basin of West Texas, United States.

Application of a Pre-Trained CNN Model for Fault Interpretation in the Structurally Complex Browse Basin, Australia

Fault detection is an important step in subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complex fault structures in seismic images, manual seismic interpretation is time-consuming and requires intensive work. We applied a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field in the Browse Basin, Australia. This field is highly structured with complex normal faulting throughout the targeted Plover Formations. Our motivation for this work is to compare machine-learning-based fault prediction to user-interpreted fault identification supported by seismic variance attributes. We found reasonably satisfactory results using CNN with an improved fault probability volume that outperforms variance technology. Therefore, we propose that this workflow could reduce time and be able to predict faults quite accurately in most structurally complex areas.

Geophysically guided well siting at the Aluto-Langano geothermal reservoir

Volcano-hosted high-temperature geothermal reservoirs are powerful resources for green electricity generation. In regions where such resources are available, geothermal energy often provides a large share of a country’s total power generation capacity. Sustainable geothermal energy utilization depends on the successful siting of geothermal wells, which in turn depends on prior geophysical subsurface imaging and reservoir characterization. Electromagnetic resistivity imaging methods have proven to be a key tool for characterizing magma-driven geothermal systems because resistivity is sensitive to the presence of melt and clays that form through hydrothermal alteration. Special emphasis is often given to the “clay cap,” which forms on top of hydrothermal reservoirs along the flow paths of convecting geothermal fluids. As an example, the Aluto-Langano volcanic geothermal field in Ethiopia is covered with 178 densely spaced magnetotelluric (MT) stations. The 3D electrical conductivity model derived from the MT data images the magma body that acts as a heat source of the geothermal system, controlling geothermal convection and formation of alteration zones (commonly referred to as clay cap) atop the geothermal reservoir. Detailed 3D imaging of the clay cap topography can provide direct insight into hydrothermal flow patterns and help identify potential “upflow” zones. At Aluto all productive geothermal wells are drilled into zones of clay cap thinning and updoming, which is indicative of underlying hydrothermal upflow zones. In contrast, nonproductive wells are drilled into zones of clay cap thickening and lowering, which is an indicator for underlying “outflow” zones and cooling. This observation is linked to fundamental characteristics of volcano-hosted systems and can likely be adapted to other geothermal fields where sufficiently detailed MT surveys are available. Therefore, high-resolution 3D electromagnetic imaging of hydrothermal alteration products (clay caps) can be used to infer the hydrothermal flow patterns in geothermal reservoirs and contribute to derisking geothermal drilling projects.

Workshop Review: Recent workshop explores near-surface modeling and imaging

Modeling of the near surface plays a significant role in subsurface imaging, reservoir characterization, and monitoring. The Near-Surface Modeling and Imaging Workshop was created to address near-surface challenges in geoscience. The workshop has been held previously in 2014, 2016, and 2019. The latest instance of the workshop, 27–29 September 2022 in Muscat, Oman, continued the tradition.

New approaches to the architectural analysis of deltaic outcrops: Implications for subsurface reservoir characterization and paleoenvironmental reconstruction

This study defines a new workflow to investigate the internal facies architecture of a river-dominated delta deposit using outcrops of the Cretaceous Panther Tongue of the Star Point Sandstone in central Utah, U.S.A. Photorealistic virtual outcrop models (VOM) were created from ~13 linear-km of outcrop. The VOMs, alongside field observations, were used to identify and map facies and facies associations over the ~25 m-thick stratigraphic interval. In order to investigate this system as a potential subsurface reservoir analog, a database of measurements was constructed using 60 digital sections that were measured within the VOMs at 152 m (~500 ft) spacing. This database characterizes a total of 508 sandstone beds by their thickness, length, and dip, from which the average thickness (0.78 m), bed length (330 m), and bed dip (2˚ towards the south) were calculated. Thinning rates were also calculated in both depositional strike and depositional dip directions (1.37x10-2 and 1.01x10-2 respectively). These comparable values suggest that the sandstone beds thin equally laterally and longitudinally from the sediment source, a characteristic typical of modern-day river-dominated deltas. The dataset also includes siltstone unit thickness data, recording an average thickness of 0.81 m, and a trend of increasing siltstone abundance and thickness moving from proximal areas where siltstones comprise 3% of the succession, and 77% in the most distal areas. The impact of observational scale was also investigated by querying the database at multiple physical length scales to include more typical well spacings of 305 and 610 m (500 and 1000 ft). This additional analysis emphasizes the role of variability in lithologically heterogeneous systems and underscores the utility of outcrop analogs to better understand sources of uncertainty. From these data, a series of depositional environment maps was constructed, illustrating the evolution of eight delta lobes that encompass the preserved depositional history of the study area. These maps document the compensational stacking of individual lobes and the progressive southward directed trajectory of the Panther Tongue delta system. Results of this analysis provide insight into depositional processes and scales of heterogeneity of the Panther Tongue and, by extension, analogous river-dominated delta systems. The workflow established in this study is exportable to other sedimentary outcrops and environments, thus demonstrating that VOMs can be used as a basis for quantitative database development and reservoir modeling inputs.

High-resolution geologic modeling and CO2 flow simulation of a realistic clastic deltaic 3D model derived from a laboratory flume tank experiment

Carbon sequestration in geologic reservoirs is a proven method for reducing carbon dioxide (CO2) emissions from industrial point sources. Subsurface deltaic deposits are attractive candidates for CO2 storage projects due to their common occurrence as host to significant hydrocarbon and hydrologic resources, and are highly prospective for subsurface storage of large volumes of CO2. This research informs subsurface passive margin deltaic reservoir characterization and fluid flow performance for geologic carbon storage. The objective of this research is to create a high-resolution geocellular digital model that honors both the large-scale stratigraphic architecture and local vertical and lateral facies heterogeneity represented in deltaic environments of deposition. We present a novel approach to reservoir model generation by utilizing a previously generated meter-scale physical flume sand tank analog experiment to represent realistic deltaic stratigraphic architectures and facies distributions. The result is an extremely high-resolution geologic model that can be used for a variety of applications, including single and multiphase fluid flow simulation. An application is presented using the example model to simulate buoyant fluid (CO2) migration and resulting saturation distribution. Results emphasize the importance of recognizing sequence stratigraphic architectures in deltaic depositional settings for predicting CO2 migration and retention (storage capacity).

Ensemble-based machine learning application for lithofacies classification in a pre-salt carbonate reservoir, Santos Basin, Brazil

Machine learning techniques have been widely used in the oil and gas industry to improve the qualitative and quantitative characterization of subsurface reservoirs. Because rock properties are strongly influenced by lithological and sedimentological information, lithofacies classification is an important step in 3D reservoir modeling. The aim of this study is to use supervised classification algorithms to predict the spatial distribution pattern of lithofacies classes using borehole and seismic data. In this study, lithofacies classes are distributed away from the wells using a machine-learning classifier. Seismic data attributes extracted from well locations are utilized as training data features in various supervised classification algorithms. Machine learning classifiers trained and evaluated for lithofacies classification include K-nearest neighbors, support vector machine, Gaussian naive Bayes, decision tree, Gradient Boosting, and Random Forests. A number of parameters are optimally determined in order to achieve the highest value of classification accuracy in the model. Comparing machine learning classifiers based on evaluation metrics reveals that ensemble-based decision tree approaches such as Random Forests and Gradient Boosting are the most effective for supervised classification. The results are validated using testing data and have an 80% classification accuracy. The predicted volume of lithofacies classes contributes to improved 3D reservoir modeling for the pre-salt carbonate reservoir.

FaciesViT: Vision transformer for an improved core lithofacies prediction

Lithofacies classification is a fundamental step to perform depositional and reservoir characterizations in the subsurface. However, such a classification is often hindered by limited data availability and biased and time-consuming analysis. Recent work has demonstrated the potential of image-based supervised deep learning analysis, specifically convolutional neural networks (CNN), to optimize lithofacies classification and interpretation using core images. While most works have used transfer learning to overcome limited datasets and simultaneously yield a high-accuracy prediction. This method raises some serious concerns regarding how the CNN model learns and makes a prediction as the model was originally trained with entirely different datasets. Here, we proposed an alternative approach by adopting a vision transformer model, known asFaciesViT, to mitigate this issue and provide improved lithofacies prediction. We also experimented with various CNN architectures as the baseline models and two different datasets to compare and evaluate the performance of our proposed model. The experimental results show that the proposed models significantly outperform the established CNN architecture models for both datasets and in all cases, achieving an f1 score and weighted average in all tested metrics of 95%. For the first time, this study highlights the application of the Vision Transformer model to a geological dataset. Our findings show that the FaciesViT model has several advantages over conventional CNN models, including (i) no hyperparameter fine-tuning and exhaustive data augmentation required to match the accuracy of CNN models; (ii) it can work with limited datasets; and (iii) it can better generalize the classification to a new, unseen dataset. Our study shows that the application of the Vision transformer could further optimize image recognition and classification in the geosciences and mitigate some of the issues related to the generalizability and the explainability of deep learning models. Furthermore, the implementation of our proposedFaciesViTmodel has been shown to improve the overall performance and reproducibility of image-based core lithofacies classification which is significant for subsurface reservoir characterization in different basins worldwide.

Direct shear-wave seismic survey in Sanhu area, Qaidam Basin, west China

The formation containing shallow gas clouds poses a major challenge for conventional P-wave seismic surveys in the Sanhu area, Qaidam Basin, west China, as it dramatically attenuates seismic P-waves, resulting in high uncertainty in the subsurface structure and complexity in reservoir characterization. To address this issue, we proposed a workflow of direct shear-wave seismic (S-S) surveys. This is because the shear wave is not significantly affected by the pore fluid. Our workflow includes acquisition, processing, and interpretation in calibration with conventional P-wave seismic data to obtain improved subsurface structure images and reservoir characterization. To procure a good S-wave seismic image, several key techniques were applied: (1) a newly developed S-wave vibrator, one of the most powerful such vibrators in the world, was used to send a strong S-wave into the subsurface; (2) the acquired 9C S-S data sets initially were rotated into SH-SH and SV-SV components and subsequently were rotated into fast and slow S-wave components; and (3) a surface-wave inversion technique was applied to obtain the near-surface shear-wave velocity, used for static correction. As expected, the S-wave data were not affected by the gas clouds. This allowed us to map the subsurface structures with stronger confidence than with the P-wave data. Such S-wave data materialize into similar frequency spectra as P-wave data with a better signal-to-noise ratio. Seismic attributes were also applied to the S-wave data sets. This resulted in clearly visible geologic features that were invisible in the P-wave data.

Information content in 4D seismic data: Effect of correlated noise

Several types of data used in history matching for subsurface reservoir characterization have errors that are spatially or temporally correlated. Although it is often assumed that correlated observation errors decrease information content, using a simplified flow problem, we show that for data that are spatially dense (such as 4D seismic data), correlated observation errors result in higher information content than data with similar levels of errors, but without correlation. Unfortunately, correlations in the observation error are often unrecognized and difficult to estimate, especially if the correlation scale of the errors is similar to a characteristic length of the signal. In addition, many history matching algorithms are incapable of accounting properly for correlated observation error, so data are either thinned or the observation error is inflated to partially account for the lack of proper treatment. We show that neglecting correlations in the observation errors or inflating the variance, result in loss of information content. Finally, we show that it is possible to iteratively estimate the correlated observation errors through analysis of residuals after history matching. • Measurement errors in seismic data are generally correlated. • Correlated errors can increase information content when measurements are dense. • Neglecting correlations during model calibration results in information loss. • Correlations in seismic data can be estimated from data mismatch after calibration.

Petrographic and diagenetic study of siliciclastic Jurassic sediments from the northeastern margin of Africa: Implication for reservoir quality

Northeastern Egypt is one of the most important exposures in the Middle East and North Africa to study Jurassic facies. The present study analyzes 130 thin sections from a subsurface well (Well X) in the northern Gulf of Suez, and from two surface sections at Gebel Maghara (north Sinai) and Khashm Elgalala (North Eastern Desert), Egypt for petrographic and lithofacies analyses. In the present study, detailed petrographic analysis is used as a tool to better understand the diagenetic history of the Early to Late Jurassic siliciclastic sediments; the identified diagenetic elements are inferred concerning the reservoir quality. For this, sidewall cores, scanning electron microscopy and thin sections are used to detail detrital and authigenic mineralogy; these are then used to infer the depositional framework, factors controlling reservoir characteristics, and the operating diagenetic processes. The inferred depositional paleoenvironment is a prograding and retrograding linear siliciclastic shoreline within a shallow marine carbonate platform with coal swamps and occasional cross-cutting rivers. Diagenesis, petrographical characteristics and depositional conditions are the main factors controlling continental and marine reservoir architecture. The diagenetic processes affecting siliciclastic sedimentation are near the surface, shallow to intermediate burial, and deep burial cycles with different pore fluid filling at each stage. The siliciclastic sediments have been categorized into seven depositional lithofacies - calcareous claystone (S1a), carbonaceous claystone (S1b), siltstone (S2), planar and trough cross-bedded sandstone (S3), coarse, well rounded, large scale trough cross-bedded sandstone (S4), ooid sandstone (S5) and coal (C). Grain compaction, various phases of syntaxial quartz overgrowth, carbonate cementation and replacement, creation of dissolution porosity, and clay authigenesis are the most important diagenetic processes that have affected the siliciclastic continental and marine sediments. Many different diagenetic events, both destructive and constructive, have modified porosity. The destructive events include compaction and cementation (of silica, kaolinite, ferroan dolomite, Illite, and anhydride). The constructive events include silica cementation, feldspar dissolution, and ferroan dolomite dissolution. Additionally, present data also suggest that the continental sandstones have excellent reservoir quality whereas the marine sandstones have good to very good reservoir potential. Despite the excellent reservoir quality of the continental sandstone lithofacies, the effective stratigraphic seal is leaking. The interbedded calcareous and carbonaceous claystone lithofacies may provide an excellent stratigraphic seal for these siliciclastic reservoirs. Based on these analyses, a model is proposed that can be used as a template for subsurface Jurassic reservoir characterization and reservoir discrimination.

Characterization of flow and transport in a fracture network at the EGS Collab field experiment through stochastic modeling of tracer recovery

Energy extraction from subsurface reservoirs is important for addressing the increasing energy demand and environmental concerns such as global warming. However, the characterization of subsurface reservoirs, particularly reservoirs dominated by fracture networks remains a challenge due to the lack of means to directly observe subsurface processes. This study explores the feasibility and efficacy of characterizing fracture flow and transport processes in an enhanced geothermal system (EGS) testbed through stochastic tracer modeling. There are two enabling factors that allow application of stochastic modeling to characterize a subsurface reservoir. First, an abundance of geological and geophysical measurements enables the development of a high-fidelity and well-constrained fracture network model. Second, high-performance computing (HPC) allows running massive realizations efficiently. Six conservative tracer tests were stochastically modeled and produced satisfactory realizations that successfully reproduce field tracer recovery data from each tracer test. The evolution of flow and transport processes in the fracture network was then analyzed from these satisfactory realizations. The present study demonstrates that stochastic tracer modeling on a high-fidelity fracture network model is feasible and can provide important insights regarding flow and transport characteristics in subsurface fractured reservoirs.

Enhancing subsurface imaging and reservoir characterization in the Marcellus Shale play, northeast Pennsylvania, through advanced reprocessing of wide-azimuth 3D seismic data

The Marcellus Shale sits in a geologically complicated region, characterized by faults and salt-cored compressional folds. Large lateral velocity variations associated with this complex geology make seismic imaging difficult. Recently acquired 3D seismic with wide azimuthal coverage and long offsets have poorly imaged folds and faults within the Marcellus Shale. Wells drilled using the existing 3D seismic volumes often encounter incorrect bed dips and folds mistakenly interpreted as faults (or vice versa) because of the poor imaging quality. We carefully examined each processing step in the seismic processing and imaging workflow and identified a few key drivers that could potentially lead to improvements in subsurface imaging. Starting from field seismic records, we identified and corrected geometry errors. To improve signal-to-noise ratio, we applied advanced noise attenuation practices including land surface-related multiple elimination. We carefully tested and validated an extension patch in the 5D seismic data interpolation that improved the offset coverage in the crossline direction. Furthermore, we performed orthorhombic prestack time migration (PSTM). An orthorhombic velocity model fits the geology better than the traditional vertical transverse isotropic (VTI) model for layered subsurface because a dominant set of orthogonal fracture sets is present in the basin. We achieved a significant improvement of subsurface imaging by implementation of these processing steps as key drivers. The new orthorhombic PSTM correctly images steeply dipping (75° and above) faults that were elusive or absent on the previous VTI migration. Additionally, previously interpreted faults are now clearly imaged as folds, small and large in scale. These imaging improvements have enabled the accurate drilling of lateral wells in the target that otherwise would have been drilled out of zone. Some large faults, which pass through the Marcellus, Mahantango, and Tully Formations, are now well-imaged but were previously barely visible. Successful imaging of such large faults helps identify and avoid geohazards. The dramatic improvement in the subsurface imaging and amplitude-preserving processing enhance the reservoir description of the Marcellus Shale.

Improving seismic fault detection by super-attribute-based classification

Fault interpretation is one of the routine processes used for subsurface structure mapping and reservoir characterization from 3D seismic data. Various techniques have been developed for computer-aided fault imaging in the past few decades; for example, the conventional methods of edge detection, curvature analysis, red-green-blue rendering, and the popular machine-learning methods such as the support vector machine (SVM), the multilayer perceptron (MLP), and the convolutional neural network (CNN). However, most of the conventional methods are performed at the sample level with the local reflection pattern ignored and are correspondingly sensitive to the coherent noises/processing artifacts present in seismic signals. The CNN has proven its efficiency in utilizing such local seismic patterns to assist seismic fault interpretation, but it is quite computationally intensive and often demands higher hardware configuration (e.g., graphics processing unit). We have developed an innovative scheme for improving seismic fault detection by integrating the computationally efficient SVM/MLP classification algorithms with local seismic attribute patterns, here denoted as the super-attribute-based classification. Its added values are verified through applications to the 3D seismic data set over the Great South Basin (GSB) in New Zealand, where the subsurface structure is dominated by polygonal faults. A good match is observed between the original seismic images and the detected lineaments, and the generated fault volume is tested usable to the existing advanced fault interpretation tools/modules, such as seeded picking and automatic extraction. It is concluded that the improved performance of our scheme results from its two components. First, the SVM/MLP classifier is computationally efficient in parsing as many seismic attributes as specified by interpreters and maximizing the contributions from each attribute, which helps minimize the negative effects from using a less useful or “wrong” attribute. Second, the use of super attributes incorporates local seismic patterns into training a fault classifier, which helps exclude the random noises and/or artifacts of distinct reflection patterns.

Integrated Reservoir Characterization and Petrophysical Analysis of Cretaceous Sands in Lower Indus Basin, Pakistan

The reservoir character of the Cretaceous sand is evaluated in Lower Indus Basin, Pakistan where water flooding is very common. Thus, prediction of subsurface structure, lithology and reservoir characterization is fundamental for a successful oil or gas discovery. Seismic reflective response is an important tool to detect sub-surface structure. Seismic reflection response is not enough to highlight geological boundaries and fluids in the pore space therefore, the use of integrated approach is vital to map sub-surface heterogeneities with high level of confidence. Based on seismic character and continuity of prominent reflectors four seismic horizons are marked on the seismic sections. All the strata is highly disturbed and distorted with presence of a network of fault bounded horst and graben structures, which indicate that the area was under compressional tectonic regime. These fault bounded geological structure formed structural traps favorable for the accumulation of hydrocarbon. The petrophysical analysis reveals that the Cretaceous sand formation has four types of sand: Sand A, B, C and D with good porosity (15 % average) and low volume of shale. Although complete petroleum system is present with structural traps and reservoir character of sand interval is very good but these sands are highly saturated with water thus are water flooded, which is the main reason of the abundant wells in the study area.

Meandering river deposits in sediment cores, the Middle Jurassic Alma Field, Southern Danish Central Graben

Fluvial deposits are amongst the most important terrestrial hydrocarbon reservoirs, but the complex nature of these deposits is challenging in subsurface reservoir characterisation. This study is the first detailed facies analysis of the meandering river deposits of the Middle Jurassic Alma Field situated in the southern Danish North Sea. The fluvial sandstones and their associated deposits are described and interpreted based on studies from two core sites (Alma-1X and Alma-2X). The facies analysis of the cores demonstrates the presence of three meandering river facies associations: Channel deposits, channel margin deposits and floodplain deposits. The channel deposits comprise channel thalweg and point bar sediments, the channel margin deposits include crevasse channel and crevasse splay sediments, while the floodplain deposits comprise overbank and backswamp sediments. The point bar deposits are composed of fine- to medium-grained sandstones but can contain intervals of finer grained sediments, particularly in their upper parts where they can grade into muddy sandstones or true heterolithic deposits. Preserved sand body thicknesses (channel thalweg and point bar deposits) in both Alma cores have a mean value of 2.6 m and a maximum value of 4.35 m (Alma-1X) and 6.55 m (Alma-2X). Using maximum values of channel deposit thicknesses, and assuming the preservation conditions are met, the width of the largest ancient channel belt in Alma-1X would be between 90 m and 200 m or around 900 m, depending on whether the fluvial system is mud-rich or sand-rich. The same method applied to Alma-2X gives a width of the largest channel belt between 130 m and 330 m or around 1300 m. Fluvial sediments of the Middle Jurassic Scalby Formation (north-east England) were deposited in a sandy meandering river with sedimentary characteristics corresponding to those observed in the Alma cores. Outcrop analogue investigations of this formation were carried out to examine the architecture of the fluvial facies in a two-dimensional section with emphasis on channel thalweg and point bar deposits. Combined evidence from core analysis and outcrop analogue studies suggests that the fluvial deposits in the Alma Field represent a mixed-load meandering river system with sandy point bars. The meandering river system developed on a coastal plain with overbank fines and organic-rich backswamp deposits. The mud-rich or heterolithic deposits in the upper part of the point bar facies intervals are noteworthy and could indicate markedly fluctuating discharge in a mixed-load river.

Internal architecture of mass‐transport deposits in basinal carbonates: A case study from southern Italy

AbstractSubmarine mass‐transport deposits represent important stratigraphic heterogeneities within slope and basinal sedimentary successions. A poor understanding of how their distribution and internal architecture affect the fluid flow migration pathway may lead to unexpected compartmentalization issues in reservoir analysis. Studies of modern carbonate mass‐transport deposits mainly focus on large seismic‐scale slope failures; however, the near‐platform basinal depositional environment often hosts mass‐transport deposits of various dimensions. The small‐scale and meso‐scale (metres to several tens of metres) carbonate mass‐transport deposits play a considerable role in distribution of sediment and therefore have an impact on the heterogeneity of the succession. In order to further constrain the geometry and internal architecture of mass‐transport deposits developed in near‐slope basinal carbonates, a structural and sedimentological analysis of sub‐seismic‐scale mass‐transport deposits has been undertaken on the eastern margin of the Apulian carbonate platform in the Gargano Promontory, south‐east Italy. These mass‐transport deposits, that locally comprise a large proportion (50 to 60%) of the base of slope to basinal sediments of the Cretaceous Maiolica Formation, typically display a vertically bipartite character, including debrites and slump deposits of varying volume ratios. A range of brittle and ductile deformation styles developed within distinct bed packages, together with the presence of both chert clasts, folded chert layers and spherical chert nodules, suggest that sediments were at different stages of lithification prior to downslope movement associated with mass‐transport deposits. This study helps elucidate the emplacement processes, frequency and character of subseismic‐scale mass‐transport deposits within the basinal carbonate environment, and thereby reduces the uncertainties in the characterization of subsurface carbonate geofluid reservoirs.