Carbonate Reservoirs Research Articles

Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches

Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity–permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.

CREATION OF AN ALGORITHM FOR THE IDENTIFICATION OF ANALOGOUS OBJECTS IN CARBONATE RESERVOIRS OF THE VOLGA-URAL OIL AND GAS PROVINCE

For the conditions of deposits confined to the carbonate reservoirs of the Volga-Ural oil and gas province, an algorithm for searching for analogous objects based on the identification procedure is proposed. The principal boundaries of the search for analogous objects confined to various stratigraphic systems have been established through a comprehensive analysis of geological and commercial data. The significant influence of tectonic confinement on the formation of the features of the geological structure of deposits and their distribution in the axes of canonical discriminant functions is shown. The presence of uncertainty in the separation of objects using the tectonic factor is explained by the similarity of objects in the marginal zones of adjacent tectonic elements in both terrigenous and carbonate reservoirs, which in turn must be taken into account when implementing key aspects of field development design. During the simulation, a certain similarity of individual deposits in the carbonate reservoirs of the carboniferous system was established, confined not only to various tectonic elements, but also to various stratigraphic complexes, which makes it possible to expand the search for analogous objects and the possibility of solving various development management tasks in conditions of deterioration of the structure of oil reserves and low technical and technological indicators of well operation. When using pattern recognition methods, in particular, discriminant analysis, dependencies were obtained that make it possible to successfully adapt the accumulated experience of developing an object to other objects while maintaining a high level of compliance with the logical aspects of hydrodynamics and physics of reservoir systems. Further development of the algorithm presented in the work lies in the plane of using a minimum number of parameters for analysis, ensuring a high percentage of separation of objects, taking into account the heterogeneity of their values.

Urban trees' potential for regulatory services in the urban environment: an exploration of carbon sequestration.

Urbanisation has emerged as a formidable challenge for urban policymakers, reaching unparalleled heights and unsettling the ecological equilibrium of the cities. Urban areas now grapple with many issues encompassing climate change, resource depletion, population surges and increased pollution levels. Many planned cities have planted trees and other vegetation within the urban sectors to enhance air quality, mitigate climate effects and provide valuable ecosystem services. This study assessed tree species diversity and their potential for carbon sequestration in Panjab University Campus, Chandigarh. We established 188 plots, each comprising randomly selected quadrats measuring 10 m × 10 m, encompassing areas with varying levels of vegetation, ranging from low to moderate and high density. We used four different allometric equations to estimate tree biomass and carbon stock. Our findings revealed that 92 tree species belong to 72 genera and 35 families, with a total tree density of 975 ha-1. The total CO2 sequestration in form of carbon stock was 18,769.46 Mg C ha-1, with Manilkara hexandra (1239.20 Mg C ha-1), Ficus benghalensis (1072.24 Mg C ha-1), Kigelia pinnata (989.89 Mg C ha-1) and Lagerstroemia floribunda (716.88 Mg C ha-1) being the top contributors. Specifically, the equation of Chave et al. (2005) without tree height yielded the highest biomass and carbon stock estimates than other equations. The present study underscores the vital role of trees on the campus as potent carbon reservoirs meet to maintain an aesthetic sense for biotic components and alleviate rising levels of CO2 in the atmospheric environment. By emphasising the role of urban trees as potent carbon reservoirs, the study underscores the importance of integrating green infrastructure into urban planning strategies. Furthermore, it offers valuable guidance for urban planners. It suggests that strategic tree planting and maintenance can enhance green spaces, regulate temperatures and ultimately support regional and global climate change mitigation goals. Incorporating these findings into urban planning processes can aid policymakers in developing resilient, ecologically sustainable cities worldwide.

Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs

Geothermal energy is a sustainable energy source that meets the needs of the climate crisis and global warming caused by fossil fuel burning. Geothermal resources are found in complex geological settings, with faults and interconnected networks of fractures acting as pathways for fluid circulation. Identifying faults and fractures is an essential component of exploiting geothermal resources. However, accurately predicting fractures without high-resolution geophysical logs (e.g., image logs) and well-core samples is challenging. Soft computing techniques, such as machine learning, make it possible to map fracture networks at a finer resolution. This study employed four supervised machine learning techniques (multilayer perceptron (MLP), random forests (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR)) to identify fractures in geothermal carbonate reservoirs in the sub-basins of East China. The models were trained and tested on a diverse well-logging dataset collected at the field scale. A comparison of the predicted results revealed that XGBoost with optimized hyperparameters and data division achieved the best performance than RF, MLP, and SVR with RMSE = 0.02 and R2 = 0.92. The Q-learning algorithm outperformed grid search, Bayesian, and ant colony optimizations. The blind well test demonstrates that it is possible to accurately identify fractures by applying machine learning algorithms to standard well logs. In addition, the comparative analysis indicates that XGBoost was able to handle the complex relationship between input parameters (e.g., DTP > RD > DEN > GR > CAL > RS > U > CNL) and fracture in geologically complex geothermal carbonate reservoirs. Furthermore, comparing the XGBoost model with previous studies proved superior in training and testing. This study suggests that XGBoost with Q-learning-based optimized hyperparameters and data division is a suitable algorithm for identifying fractures using well-log data to explore complex geothermal systems in carbonate rocks.Graphical abstract

Discovery and characterization of l-DOPA 2,3-dioxygenase from Streptomyces hygroscopicus jingganensis

The largest natural reservoir of untapped carbon can be found in the cell-wall strengthening, plant woody-tissue polymer, lignin - a polymer of catechols or 1,2-dihydroxybenzene monomers. The catecholic carbon of lignin could be valorized into feedstocks and natural products by way of catabolic and biosynthetic transformations, including the oxygen-dependent cleavage reaction of extradiol dioxygenase (EDX) enzymes. The EDX l-DOPA 2,3-dioxygenase was first discovered as part of a biosynthetic gene cluster to the natural product antibiotic, lincomycin, and also contributes to the biosyntheses of anthramycin, sibiromycin, tomaymycin, porothramycin and hormaomycin. Using these l-DOPA 2,3-dioxygenases as a starting point, we searched sequence space in order to identify new sources of dioxygenase driven natural product diversity. A “vicinal-oxygen-chelate (VOC) family protein” from Streptomyces hygroscopicus jingganensis was identified using bioinformatic methods and biochemically investigated for dioxygenase activity against a suite of natural and synthetic catechols. Steady-state oxygen consumption assays were used to screen and identify substrates, and a steady-state kinetic model of oxygen consumption was developed to evaluate activity of the S. hygroscopicus jingganensis VOC-family-protein with respect to activity of l-DOPA 2,3-dioxygenases from Streptomyces lincolnensis and Streptomyces sclerotialus. Lastly, these data were integrated with steady-state kinetic methods to observe the formation of the EDX cleavage product with UV–visible spectroscopy. The genomic context and enzymatic activity of the S. hygroscopicus jingganensis VOC family protein are consistent with a l-DOPA 2,3-dioxygenase contained within a cryptic biosynthetic pathway.

Low-Salinity Waterflooding for EOR in Field A of Western Offshore Basin: A Pilot Study Analysis with Laboratory and Simulation Studies—Early Observations

Carbonate reservoirs hold vast oil reserves, but their complex properties make traditional enhanced oil recovery (EOR) methods challenging. This study explores the application of low-salinity water flooding (LSWF) as a novel EOR method for India’s largest offshore carbonate oil field. Conventional EOR techniques were deemed unsuitable due to reservoir heterogeneity, pressure decline, high temperature, and the offshore location. Favorable factors for LSWF included successful seawater flooding history, medium-weight crude oil, and existing infrastructure. Following core flooding experiments demonstrating a 6–16% increase in oil recovery, a multi-pronged evaluation process was implemented. Single-well chemical tracer tests (SWCTT) and reservoir simulations confirmed the potential of LSWF. A specific target area was chosen based on reservoir characteristics, production data, and available facilities. Simulations predicted a 1.5% incremental oil recovery using diluted seawater (25% salinity) at 30% pore volume injection. After a positive techno-economic analysis, the first offshore LSWF project in India was completed within 3 years. Initial monitoring results are encouraging. This study highlights the successful journey of LSWF from concept to field deployment in a challenging carbonate reservoir, showcasing its potential for revitalizing such fields. Furthermore, this work provides valuable data relevant to Indian offshore environments, where factors like salinity, mineralogy, and crude oil composition pose unique challenges compared to other LSWF applications. These detailed data fill a critical gap in the existing literature.

Evaluation of the Oil Recovery Potential and Cost Implication Analysis of Alternative ASP Formulations for Sandstone and Carbonate Reservoirs

Evaluation of the Oil Recovery Potential and Cost Implication Analysis of Alternative ASP Formulations for Sandstone and Carbonate Reservoirs

Analysis of Seismic Response Characteristics of Fractured Carbonate Reservoirs Based on Physical Model (Tarim Basin)

Anomalous bright spots, called the string of bead-like response, are typical seismic migration profile features in carbonate fractured reservoirs in the Tarim Basin, and they are indicators of high-quality oil and gas reservoirs. Correctly recognizing the correspondence between fractures and the SBLR can contribute to the efficient drilling of target carbonate fractured reservoirs. Physical models can describe fractured reservoirs more directly and efficiently than real situations and have obvious advantages in accurately and quantitatively designing parameters such as dipping angles and the number of layers of fractured reservoirs. Under such a background, according to the real tectonic characteristics of the Tarim Basin, among the main hydrocarbon reservoirs, fractured reservoirs with various parameters were designed and a physical model was constructed according to the real stratigraphic parameters. After seismic data acquisition and processing, the response characteristics of the string of bead-like response were extracted and summarized from seismic migration profiles for all fractured reservoirs, which provided targeted analyses and discussions on the fracture dipping angle, number of fracture layers, overlying stratigraphic influences, and planar attributes of the fractured reservoirs. In general, the larger the fracture dip, the more difficult it is to identify, while the slope of reflection strength and maximum absolute amplitude attributes can be important markers for fractured reservoir identification. The physical modeling study of fractured reservoirs in this paper can provide a basis for the analysis and prediction of carbonate fractured reservoirs in the Tarim Basin.

Atmospheric constraints on changing Arctic CH4 emissions

Rapid warming in the Arctic has the potential to release vast reservoirs of carbon into the atmosphere as methane (CH4) resulting in a strong positive climate feedback. This raises the concern that, after a period of near-zero growth in atmospheric CH4 burden from 1999 to 2006, the increase since then may be in part related to increased Arctic emissions. Measurements of CH4 in background air samples provide useful, direct information to determine if Arctic CH4 emissions are increasing. One sensitive first-order indicator for large emission change is the Interpolar Difference, that is the difference in surface atmospheric annual means between polar northern and southern zones (53°–90°), which has varied interannually, but did not increase from 1992 to 2019. The Interpolar Difference has increased moderately during 2020–2022 when the global CH4 burden increased significantly, but not yet to its peak values in the late-1980s. For quantitative assessment of changing Arctic CH4 emissions, the atmospheric measurements must be combined with an atmospheric tracer transport model. Based on multiple studies including some using CH4 isotopes, it is clear that most of the increase in global atmospheric CH4 burden is driven by increased emissions from microbial sources in the tropics, and that Arctic emissions have not increased significantly since the beginning of our measurement record in 1983 through 2022.

Pre-Stack Fracture Prediction in an Unconventional Carbonate Reservoir: A Case Study of the M Oilfield in Tarim Basin, NW China

The reservoir of the M oilfield in Tarim Basin is an unconventional fracture-cave carbonate rock, encompassing various reservoir types like fractured, fracture-cave, and cave, exhibiting significant spatial heterogeneity. Despite the limited pore space in fractures, they can serve as seepage pathways, complicating the connectivity between reservoirs. High-precision fracture prediction is critical for the effective development of these reservoirs. The conventional post-stack seismic attribute-based approach, however, is limited in its ability to detect small-scale fractures. To address this limitation, a novel pre-stack fracture prediction method based on azimuthal Young’s modulus ellipse fitting is introduced. Offset Vector Tile (OVT) gather is utilized, providing comprehensive information on azimuth and offset. Through analyzing azimuthal anisotropies, such as travel time, amplitude, and elastic parameters, smaller-scale fractures can be detected. First, the original OVT gather data are preprocessed to enhance the signal-to-noise ratio. Subsequently, these data are partially stacked based on different azimuths and offsets. On this basis, pre-stack inversion is carried out for each azimuth to obtain the Young’s modulus in each direction, and, finally, the ellipse fitting algorithm is used to obtain the orientation of the long axis of the ellipse and the ellipticity, indicating the fracture orientation and density, respectively. The fracture prediction results are consistent with the geological structural features and fault development patterns of the block, demonstrating good agreement with the imaging logging interpretations. Furthermore, the results align with the production dynamics observed in the production wells within the block. This alignment confirms the high accuracy of the method and underscores its significance in providing a robust foundation for reservoir connectivity studies and well deployment decisions in this region.

Earth system responses to carbon dioxide removal as exemplified by ocean alkalinity enhancement: tradeoffs and lags

Abstract Carbon Dioxide Removal (CDR) is discussed for offsetting residual greenhouse gas emissions or even reversing climate change. All emissions scenarios of the Intergovernmental Panel on Climate Change that meet the ”well below 2 ◦C” warming target of the Paris Agreement include CDR. Ocean alkalinity enhancement (OAE) may be one possible CDR where the carbon uptake of the ocean is increased by artificial alkalinity addition. 
Here, we investigate the effect of OAE on modelled carbon reservoirs and fluxes in two observationally-constrained large perturbed parameter ensembles. OAE is assumed to be technically successful and deployed as an additional CDR in the SSP5-3.4 temperature overshoot scenario. Tradeoffs involving feedbacks with atmospheric CO2 result in a low efficiency of an alkalinity-driven atmospheric CO2 reduction of -0.35 [-0.37 – -0.33] mol C per mol alkalinity addition (skill-weighted mean and 68% c.i.). The realized atmospheric CO2 reduction, and correspondingly the efficiency, is more than two times smaller than the direct alkalinity-driven enhancement of ocean uptake. The alkalinity-driven ocean carbon uptake is partly offset by the release of carbon from the land biosphere and a reduced ocean carbon sink in response to lowered atmospheric CO2 under OAE.
In a second step we use the Bern3D-LPX model in CO2 peak-decline simulations to address hysteresis and temporal lags of surface air temperature change (ΔSAT) in an idealized scenario where ΔSAT increases to ∼2 ◦C and then declines to ∼1.5 ◦C as result of CDR. ΔSAT lags the decline in CO2-forcing by 18 [14 – 22] years, depending close to linearly on the equilibrium climate sensitivity of the respective ensemble member. These tradeoffs and lags are an inherent feature of the Earth system response to changes in atmospheric CO2 and will therefore be equally important for other CDR methods.

Tight carbonate reservoir evaluation case study based on neural network assisted fracture identification and analytic hierarchy process

AbstractComprehensive evaluation of reservoirs is an important link in gas reservoir exploration and development. The evaluation of tight carbonate reservoirs often focuses on the characteristics of porosity and permeability, ignoring the important factor of fractures, also the quantitative evaluation of reservoirs is relatively few. It is difficult to identify fractures and evaluate the reservoir factors qualitatively and quantitatively. Herein, the sedimentary microfacies and microporosity of the tight carbonate reservoir of the Ma55 submember in the eastern Sulige area are comprehensively studied by casting thin section, rock physical property, and capillary pressure test data. The backpropagation (BP) neural network algorithm is used to identify and predict fractures. Finally, through the analytic hierarchy process, the above reservoir influencing factors are modeled and quantitatively analyzed for reservoir evaluation. The results show that the highest probability of fracture development in the central and northwest areas of the study area can reach 0.92. The accuracy of the BP neural network model in identifying cracks can reach 80%, which is reliable and effective compared with the conventional logging identification method. Reservoirs can be classified into four types according to their quality. The synthetic weights of porosity, permeability and fracture development probability are 0.2, 0.2 and 0.216 respectively, which are the three most important evaluation parameters. This study improves the accuracy of fracture identification and prediction of tight reservoirs in comprehensive reservoir evaluation, which provides guidance and scheme for more detailed exploration and development of tight carbonate reservoirs.

The long-term impact of transgressing planetary boundaries on biophysical atmosphere–land interactions

Abstract. Human activities have had a significant impact on Earth's systems and processes, leading to a transition of Earth's state from the relatively stable Holocene epoch to the Anthropocene. The planetary boundary framework characterizes major risks of destabilization, particularly in the core dimensions of climate and biosphere change. Land system change, including deforestation and urbanization, alters ecosystems and impacts the water and energy cycle between the land surface and atmosphere, while climate change can disrupt the balance of ecosystems and impact vegetation composition and soil carbon pools. These drivers also interact with each other, further exacerbating their impacts. Earth system models have been used recently to illustrate the risks and interacting effects of transgressing selected planetary boundaries, but a detailed analysis is still missing. Here, we study the impacts of long-term transgressions of the climate and land system change boundaries on the Earth system using an Earth system model with an incorporated detailed dynamic vegetation model. In our centennial-scale simulation analysis, we find that transgressing the land system change boundary results in increases in global temperatures and aridity. Furthermore, this transgression is associated with a substantial loss of vegetation carbon, exceeding 200 Pg C, in contrast to conditions considered safe. Concurrently, the influence of climate change becomes evident as temperatures surge by 2.7–3.1 °C depending on the region. Notably, carbon dynamics are most profoundly affected within the large carbon reservoirs of the boreal permafrost areas, where carbon emissions peak at 150 Pg C. While a restoration scenario to reduce human pressure to meet the planetary boundaries of climate change and land system change proves beneficial for carbon pools and global mean temperature, a transgression of these boundaries could lead to profoundly negative effects on the Earth system and the terrestrial biosphere. Our results suggest that respecting both boundaries is essential for safeguarding Holocene-like planetary conditions that characterize a resilient Earth system and are in accordance with the goals of the Paris Climate Agreement.

Seismic lithology and geological implications of pre-salt jurassic carbonate rocks in the Amu Darya Right Bank

The formation and evolution of oil and gas reservoirs are typically related to the environment where sedimentary facies occur and regional sedimentary facies are distributed. The accurate characterization of reef–shoal complexes is one of the important tasks for exploring oil and gas in carbonate reservoirs. However, most traditional methods for characterizing sedimentary facies are based on high-density drilling data, core data, and thin-section data, often making them less suitable in areas with few drilled wells or sparse well networks. Hence, they often cannot characterize sedimentary facies accurately, severely hindering the oil and gas exploration success rate. This study seeks to characterize sedimentary facies using limited amount of data from drilled wells. An innovative model representing the relationship between lithofacies and seismic attributes was established by comprehensively analyzing the relationship among lithologic associations, principal component analysis, and regression analysis based on seismic lithology and data from various sources. The sedimentary facies of the Upper Jurassic (Oxfordian) formation in the eastern region of the Amu Darya Right Bank were accurately characterized using Red-Green-Blue (RGB) fusion. These analyses indicate that the relationship among micrite, bioclastic micrite, and bioclastic limestone can effectively represent the developmental pattern of sedimentary facies in the study area. The planar distribution of sedimentary facies based on the relationship between the lithologic associations identified through seismic interpretation revealed the relationship between sedimentary facies and the paleomorphology of the region. Results show that the paleo-landform in the study area's western zone is low-lying, where shoals are poorly developed; the paleo-landform in the central zone is high, with high-energy water and well-developed and widely distributed thick shoals; and the eastern zone is far from the open sea and affected by the central-zone landform, with low-energy water and low-quality reef–shoal complexes. Controlled by the unique landforms of the study area, the western reservoirs are thin and have low quality, central reservoirs are thick with good petrophysical properties, and eastern reservoirs have differing thickness and petrophysical properties.

Dynamics of methane emissions from northwestern Gulf of Mexico subtropical seagrass meadows

AbstractWhile seagrass meadows are perceived to be pertinent blue carbon reservoirs, they also potentially release methane (CH4) into the atmosphere. Seasonal and diurnal variations in CH4 emissions from a subtropical hypersaline lagoon dominated by Halodule wrightii in southern Texas, USA, on the northwest coast of the Gulf of Mexico were investigated. Dissolved CH4 concentrations decreased in the daytime and increased overnight during the diel observation period, which could be explained by photosynthesis and respiration of seagrasses. Photosynthetic oxygen was found to significantly reduce CH4 emissions from seagrass sediment. Diffusive transport contributed slightly to the release of CH4 from the sediment to the water column, while plant mediation might be the primary mechanism. The diffusive CH4 flux at the sea-air interface was 12.3–816.2 µmol/m2 d, over the range of the sea-air fluxes previously reported from other seagrass meadows. This was related to relatively higher dissolved CH4 concentrations (11.6–258.2 nmol/L) in a mostly closed lagoon with restricted water exchange. This study emphasizes seagrass meadows in the subtropical hypersaline lagoon as a source of atmospheric CH4, providing insights into the interactions between seagrass ecosystems and methane dynamics, with potential implications for seagrass meadow management and conservation efforts.

Effect of hollow natural fiber (HNF) content on the CO2 diffusion, carbonation, and strength development of reactive magnesium cement (RMC)-based composites

Reactive magnesia cement (RMC) is an emerging class of green cement that hardens by sequestering CO2. However, CO2 diffusion into RMC is restricted to a few millimeters by the carbonation-induced dense microstructure on the outer layer, which severely slows down the strength growth and CO2 sequestration. To address this issue, this work employed hollow natural fibers (HNFs) to facilitate CO2 diffusion into the deep regions of RMC. The effects of HNFs contents on the mechanical strength development, holistic porosity, CO2 sequestration, CO2 diffusivity, and microstructure of RMC were investigated through different techniques. The findings revealed that the compressive strength could be more than doubled with the addition of adequate sisal fiber. Moreover, the CO2 sequestration and diffusivity could be continuously enhanced with the increasing HNFs content. However, overdosage of HNFs could induce a higher porosity and additional defects, which slightly compromises the mechanical strength. Finally, the durability of HNFs in simulated RMC and Portland cement (PC) environment was compared by accelerated aging test, showing that the alkaline-induced deterioration of HNFs could be almost eliminated in RMC. Therefore, this preliminary study reinforces the function of RMC as a carbon reservoir and lays the foundation for the large-scale utilization of HNFs in RMC.

Numerical Simulation Analysis of Control Factors on Acoustic Velocity in Carbonate Reservoirs

Numerical Simulation Analysis of Control Factors on Acoustic Velocity in Carbonate Reservoirs

Evaluation of particulate organic carbon from MODIS-Aqua in a marine-coastal water body.

La Paz Bay (LPB) in Mexico is one of the largest marine-coastal bodies of water in the Gulf of California (GC) and is ecologically important for the feeding, reproduction, and refuge of marine species. Although particulate organic carbon (POC) is an important reservoir of oceanic carbon and an indicator of productivity in the euphotic zone, studies in this region are scarce. This study evaluates the performance of satellite-derived POC in LPB from January 2003 to December 2020. The metrics obtained for COP ( ; y ), Chla-a ( ; y ), and SST ( ; y ) establish that although in some cases there was a slight over/underestimation, the satellite estimates consistently represent the variability and average values measured in situ. On the other hand, the spatio-temporal analysis of the POC allowed us to identify two seasons with their respective transition periods and five subregions in which the POC is characterized by having its maximum variability; two of these coincide with the locations of the eddies reported for the winter and summer seasons in the LPB, while the following three are located: one in the coastal zone and in the two areas in which the LPB interacts with the GC. The associations, variability nodes, and multiple linear regression analysis suggest that POC fluctuations in the LPB respond mainly to biological processes and, to some extent, to the seasonality of SST and wind. Finally, our results justify the use of the MODIS-Aqua satellite POC for studies in marine-coastal water bodies with similar characteristics to the LPB and suggest that this water body can be considered a reservoir for the marine region of northwestern Mexico.

Description of Pore Structure of Carbonate Reservoirs Based on Fractal Dimension

Description of Pore Structure of Carbonate Reservoirs Based on Fractal Dimension

Reservoir characterization of the Abu Roash D Member through petrography and seismic interpretations in Southern Abu Gharadig Basin, Northern Western Desert, Egypt

This research combines petrography and seismic analysis to assess the Upper Cretaceous Abu Roash (AR)/D’s carbonate member composition in the Southwest Abu-Sennan oil field in the Southern Abu Gharadig Basin within the Northern Western Desert of Egypt. Various datasets were used, including petrographic thin sections, and electrical well logs for four stratigraphic wells (01, -02, -03, and, -04), along with a time domain seismic dataset covering the study area. Petrographic analysis across multiple depths and intervals has provided valuable insights. Well-01 demonstrates mud-wackstone with diverse mineral components at 1671–74 m MD, indicating favorable reservoir quality. Well-02 exhibits diverse compositions at intervals 1740–43 m MD and 1746–49 m MD, also showcasing good reservoir quality. Well-03 reveals a packstone rock type at 1662–65 m MD with favorable reservoir characteristics. Well-04 displays peloids Wack-Packstone and Oolitic Packstone at intervals 1764–67 m MD and 1770–73 m MD, respectively, both indicating good reservoir quality. Integrating the petrography and seismic attributes results concerning the structural level of AR/D concerning the used wells, it's evident that Well-03 stands out due to its relatively high structural level, drilled near a major fault, revealing distinct fracture sets that contribute to a notably high reservoir quality as depicted in the RMS amplitude and Ant track attributes maps. AR/D reservoir levels in wells 02, and, 04, are relatively positioned at structurally lower levels, and face challenges with overburden pressure and mechanical compaction, resulting in diminished facies quality for the reservoir. Seismic attributes like the Ant track and RMS amplitude indicated that the presence of fractures within the AR/D Member's carbonate is linked to the prevalence of interpreted normal faults. The implemented procedure in this research can be applied to enhance comprehension of AR/D carbonate reservoirs in adjacent regions, thereby increasing the hydrocarbon exploration possibilities.