Well-log Data Research Articles

Calculation and application of elastic modulus of mineral components in tight sandstone based on adaptive method

Abstract For the petrophysics modelling of tight sandstones, the elastic modulus of their sandstone and mudstone components are often substituted with those of quartz and clay, which affects model accuracy. To solve this problem, we innovate an adaptive approach for modelling the rock physics characteristics of tight sandstone. First, based on the relationship between P- and S-wave velocities from well logs and the elastic modulus of the rocks, the equivalent elastic modulus of tight sandstone under saturated conditions is calculated. Next, The Lee model and the Gassmann equation were jointly used to determine the equivalent elasticity modulus of tight sandstone matrix. The upper and lower limits of the equivalent elasticity modulus for mudstone and sandstone are established using the mudstone content curve, the least squares method and the Voigt–Reuss–Hill (V-R-H) model. We used the random-walk algorithm to accurately calculate the equivalent elastic modulus of the sandstone and mudstone components. Finally, using the accurately obtained elastic modulus of sandstone and mudstone, the equivalent elastic modulus of the matrix is calculated. The Kuster–Toksöz (K-T) model is subsequently applied to compute the dry frame bulk modulus and shear modulus of the rock. Following this, the Brie model is used to calculate the elastic modulus of the mixed fluid, thereby completing the construction of the rock physics model for the study area. The results demonstrate that our improved petrophysics model can predict the S-wave velocity curve with ≤ 15% errors relative to the true curve. When sweet spot prediction was performed using reservoir-sensitive parameter (Vp/Vs) derived from the petrophysics template, the agreement between the predictions and well-log data was 80%. Thus, our petrophysics modelling method can be used to predict tight gas reservoirs effectively and will aid efforts to improve petroleum exploration works in the study area.

The influence of major faults and fractures on the development of non-matrix porosity system in a pre-salt carbonate reservoir, Santos Basin – Brazil

Faults and fractures are central for characterizing the permeability distribution in carbonate reservoirs since they act as pathways for diagenetic fluids that often favor intense rock dissolution and permeability. Usually, high permeability zones and fractures are not easily recognized in seismic data due to limited resolution and they are often associated with higher concentrations of hydrocarbons or even significant fluid losses during drilling, thus creating a challenge for hydrocarbon exploration. In the Santos Basin, southeast Brazil, the pre-salt carbonate reservoirs from the Barra Velha Formation (BVE) are the main hydrocarbon producers in Brazilian Atlantic margin and well-known for being extremely heterogeneous, exhibiting complex dual-porosity systems. In this study, we built a conceptual model of these fracture zones and non-matrix porosity formation that helped narrowing the understanding of these complex systems. The characterization of faults and fractures was carried out using seismic, well-logs, and borehole image data to understand the influence of these structures in the porosity formation along the Barra Velha Formation. In the study area, three fault sets were defined (F1, F2, and F3) from seismic data. F1 represents to the larger faults, while the F3 fault set represents the smaller faults related to the reactivation of F1; both sets being oriented NE-SW. The F2 fault set is associated with the rift formation and is oriented to NNE-SSW. These three fault sets compartmentalized the studied area into different domains, each exhibiting distinct fracture sets. The natural open fractures were formed during the reactivation of rift faults and are oriented mainly NW, NNE-NNW, NE, and ENE and were identified across the entire study area, but with different intensity values. The fracture intensity closely relates to the distance from major faults where the wells with the highest fracture intensity occurs located 150–590 m from the larger F1 fault set. Scan-lines were conducted throughout the area to determine the fault width, and an average value of 1.2 km was established. Seven non-matrix porosity classes were characterized and classified into stratigraphically concordant and discordant non-matrix pore types at well scale through borehole image interpretation. The Barra Velha Formation exhibit higher occurrence of stratigraphically discordant non-matrix porosity related to fractured zones while stratigraphically concordant non-matrix porosity is mainly controlled by the paleotopography of the study area. Overall, non-matrix porosity formation tends to follow an orientation that suggests a preferential dissolution flow towards NE and ENE directions. Intervals with higher silica content shows a positive correlation with both fracture intensity and stratigraphically discordant non-matrix porosities. This work provides a conceptual model about the fractures and non-matrix porosity distribution in pre-salt carbonate rocks that can help address some of associated structural and stratigraphic uncertainties during field appraisal and development.

Exploring comparative heterogeneity management for precise water saturation assessment in carbonate formations: Dean-Stark measurements and beyond

Accurate determination of water saturation is a critical aspect of reservoir characterization and development potential of a hydrocarbon field. The inherent heterogeneity in carbonate reservoirs significantly influences the Archie equation coefficients and, consequently, water saturation calculations. In this study, 160 direct core sample water saturation measurements using the Dean-Stark method have been used. The primary goal was to identify the most suitable reservoir heterogeneity management approach for water saturation calculation. The research focuses on the Dalan and Kangan formations (equivalent to Khuff Formation), known as some of the most hydrocarbon-rich (gas) reservoirs globally, located in the western part of the Persian Gulf. To manage reservoir heterogeneity, we employed various techniques, including Winland R35, flow zone indicator, Lucia, pore types, and electrofacies, utilizing porosity and permeability core data, thin section studies, and well-logging data to classify the studied intervals into more homogeneous categories. Subsequently, we measured Archie parameters and, for the first time, water saturations derived from Dean-Stark measurements were compared with Archie saturations from various heterogeneity management methods. Additionally, the influence of geological and petrophysical parameters on the accuracy of water saturation calculations in different rock types is discussed. Our results highlighted the significance of geometrical attributes of pore types, pore throat radius, and permeability as key factors affecting the precision of water saturation calculations. Moreover, our investigation revealed that Lucia's rock typing methodology exerted a substantial influence on the control of permeability and the distribution of water saturation within the reservoir. Our analysis revealed that the electrofacies method provided the most precise estimates for water saturation, with a mean difference of 0.07 (v/v) units compared to the Dean-Stark method. Subsequently, the accuracy of predicted water saturation in rock types determined by the Lucia method, with a mean error of 0.09 (v/v), ranked second among the rock typing methods. Winland R35 and flow zone indicator methods exhibited the highest uncertainty in water saturation estimation, with mean differences of 0.23 and 0.21 (v/v), respectively, compared to the Dean-Stark method.

Efficient self-attention based joint optimization for lithology and petrophysical parameter estimation in the Athabasca Oil Sands

Accurately identifying lithology and petrophysical parameters, such as porosity and water saturation, are essential in reservoir characterization. Manual interpretation of well-log data, the conventional approach, is not only labor-intensive but also susceptible to human errors. To address these challenges of lithology identification and petrophysical parameter estimation in the Athabasca Oil Sands area, this study introduces an AutoRegressive Vision Transformer (ARViT) model for lithology and petrophysical parameter prediction. The effectiveness of ARViT lies in its self-attention mechanism and its ability to handle data sequentially, allowing the model to capture important spatial dependencies within the well-log data. This mechanism enables the model to identify subtle spatial and temporal relationships among various geophysical measurements. The model is also interpretable and can serve as an assistive tool for geoscientists, enabling faster interpretation while reducing human bias. The interpretable nature of the model should assist geoscientists in conducting faster quality checks of the predictions, ensuring that errors are not propagated to subsequent stages. This study adopts a multitask learning approach, jointly optimizing the model's performance across multiple tasks simultaneously. To evaluate the effectiveness of the ARViT model, we conducted series of experiments and comparisions, testing it against traditional artificial neural networks (ANN), Long Short-Term Memory (LSTM), and Vision Transformer (ViT) models. To showcase the versatility of ARViT, we apply Low-Rank Adaptation (LoRA) to a different smaller dataset, showing its potential to adapt to different geological contexts. LoRA not only helps in model adaptability but also helps to reduce the number of trainable parameters. Our findings demonstrate that ARViT outperforms ANN, LSTM, and ViT in estimating lithological and petrophysical parameters. While lithology prediction has been a well-explored field, ARViT's unique blend of features, including its self-attention mechanism, autoregression, and multitask approach along with efficient fine tuning using LoRA, sets it apart as a valuable tool for the complex task of lithology prediction and petrophysical parameter estimation.

Seismic reflection data interpretation and petrophysical evaluation of the Meyal area, the Potwar Basin, Pakistan

Despite extensive exploration efforts in the Potwar Basin, a thorough understanding of the structural framework and reservoir characteristics within the Meyal Field remains critical for pinpointing potential hydrocarbon zones. This study aims to integrate geology and geophysics to emphasize the structural and stratigraphic interpretation of the Meyal Field, including reservoir characterization using seismic and well-log data. The seismic interpretation of six seismic lines focuses on four key reflectors: the Kamlial, Chorgali, Sakesar, and Salt Range Formations. The Eocene Chorgali and Sakesar Formations are of primary importance for exploration and production. Structural analysis revealed that the Meyal anticline is a plunging structure bounded by a back thrust to the north and a fore thrust to the south, suggesting a favorable location for hydrocarbon accumulation. Time-depth contour maps derived from seismic data further delineate potential sites for future investigations. The well correlation indicated an uplift toward the Meyal-2 compared with Meyal-5, signifying a shallowing trend attributed to thrust tectonics. This tectonic regime has rendered the area highly prospective for hydrocarbon exploration, with thrusting and oblique-slip faulting enhancing the reservoir qualities of Eocene Formations. The petrophysical evaluation revealed favorable reservoir characteristics, including an average porosity ranging from 0% to 12% with an effective porosity of approximately 7.5%, water saturation up to 42%, and hydrocarbon saturation reaching 58% within the pay zone of the reservoir. These findings suggest that the Sakesar and Chorgali Formations within the Meyal oil field hold promise as productive hydrocarbon reservoirs. The integrated study provides valuable insights into the structural framework and reservoir characterization, highlighting the potential of the Eocene Formations as productive hydrocarbon reservoirs supported by favorable structural configurations and petrophysical properties.

Seismic scattering inversion for multiple parameters of overburden-stressed isotropic media

Subsurface reservoirs are compressed by overburden stress resulting from the gravity of overlying masses, and the resulting stress changes significantly affect the seismic reflection responses generated at the reservoir interfaces. Several exact reflection coefficient equations have been well established to delineate the role that in situ stress plays in altering the energy transition and amplitude of seismic reflection responses. These exact equations, however, cannot be effectively used in practice due to their intricate formulations and the difficult geophysical estimation for the third-order elastic constants (3oECs) embedded in reflection coefficients. Based on the theories of nonlinear elasticity and elastic wave inverse scattering, we derive an approximate seismic reflection coefficient equation for overburden-stressed isotropic media in terms of the P-wave modulus, shear modulus, density, and a defined stress-related parameter (SRP). The SRP is a combined quantity of elastic moduli, 3oECs, and overburden stress, which can be naturally treated as a dimensionless stress-induced anisotropy parameter. Its inclusion effectively eliminates the need for 3oECs information when using our equation to estimate the desired reservoir properties from seismic observations. By comparing our equation to the exact one, we confirm its validity within the moderate-stress range. Then, we introduce a Bayesian inversion approach incorporating the new reflection coefficient equation to estimate four model parameters. In our approach, the Cauchy and Gaussian distribution functions are used for the a priori probability and the likelihood distributions, respectively. The synthetic tests from two well-log data sets and a field example demonstrate that four parameters can be reasonably inverted using our approach with rather smooth initial models, which illustrates the feasibility of our inversion approach.

Research on porosity prediction method for petroleum reservoirs based on improved gene expression algorithm

Abstract Porosity, an essential parameter of reservoir properties, plays a crucial role in reservoir evaluation. Indirect prediction of reservoir porosity using well-logging data is a fundamental task in assessing hydrocarbon reserves. Multivariate stepwise regression and artificial neural network methods are currently typical prediction approaches. However, these methods suffer from drawbacks such as high workload, extensive training sample requirements, and high computational costs. Genetic Expression Programming (GEP), a novel genetic algorithm, exhibits remarkable function discovery capabilities, high efficiency, and the ability to function without requiring large training samples. This paper proposes a method that utilizes GEP to mine a porosity calculation model from well-logging sample data and subsequently compute the porosity. The effectiveness and efficiency of this method are validated through actual well-logging data.

Effects of overconsolidation on seismic rock physics — A comparative study between unconsolidated sands and weakly cemented sandstone

In seismic rock physics, models typically assume rocks are normally consolidated, often neglecting the effects of overconsolidation. Recent studies reveal that overconsolidated sands, resulting from stress release, show different physical property variations than normally consolidated sands. However, similar research is lacking for weakly cemented sandstones, a common reservoir analog. This study aims to systematically compare the effects of overconsolidation and stress direction on unconsolidated sandstones and weakly cemented sandstones. It also assesses the impact of overconsolidation on seismic interpretation, monitoring, and rock physics modeling applied to the two types of rocks. Experimental measurements of porosity, P-wave, and S-wave velocities were collected under different stress paths between loading and unloading. For weakly cemented sandstone, well-log data from the Norwegian Sea and Barents Sea were compared with experimental data. Analyses focused on the velocity-porosity relationship, VP/VS ratio vs. AI, and VP/VS ratio vs. effective stress.

Seismic Facies-Controlled Porosity Prediction in a Tight Sandstone Reservoir Based on the XGBoost Algorithm

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 propose a seismic facies-controlled porosity prediction method based on the extreme gradient boosting algorithm. Through seismic meme inversion method,we obtain high-resolution P-impedance data. In the inversion process, lateral variations of seismic waveforms were utilized 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 travel time, density, gamma rays, and spontaneous potential well-log data and post-stack 3D seismic data as inputs, allowing for quick porosity predictions in the field. Compared to the seismic facies-controlled inversion combined with support vector machine and multi-Layer perceptron methods, the method proposed in this study provides more reliable porosity predictions, offering insights and references for the exploration and development of tight sandstone reservoirs.

Petrophysical parameter estimation using a rock-physics model and collaborative sparse representation

The estimation of petrophysical parameters is key in the identification of underground reservoirs. Current petrophysical parameter estimation methods are typically constrained by the choice of particular rock-physics models, necessitating the use of distinct models for various reservoirs. Furthermore, the inherent pronounced nonlinearity of these models presents significant challenges to the solution process in reservoir parameter inversion. Although linearized petrophysical inversion methods can simplify the solving process, they can introduce errors due to linearization. To address these limitations, we develop a petrophysical parameter estimation method driven by a rock-physics model and collaborative sparse representation (CSR). In this approach, the rock-physics model governs the fundamental pattern of the petrophysical parameters, with the CSR introducing perturbations to minimize model errors. Our method maximizes the use of well-log data and the rock-physics model, thereby reducing errors associated with linearized rock-physics (LRP) models and enhancing the method’s adaptability. We use the joint dictionary method to learn features and correlations among multiple parameters from the existing well-log data. This learned dictionary is then used as a CSR regularization constraint and applied to refine the LRP inversion objective function. Finally, the objective function is minimized using the block coordinate descent method to predict the petrophysical parameters. The effectiveness of this method in enhancing the adaptability and accuracy of petrophysical parameter estimation is confirmed through synthetic and field data tests.

Control and prediction of bedding-parallel fractures in fine-grained sedimentary rocks: A case from the Permian Lucaogou Formation in Jimusar Sag, Junggar Basin, Western China

The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration, enrichment, and efficient development of oil and gas. However, because of their variety and the complexity of the factors that affect them, their spatial prediction by the industrial community becomes challenging. Based on sample cores, thin sections, and well-logging and seismic data, this study employed a multi-scale data matching approach to quantitatively predict the development of bedding-parallel fractures and investigate their spatial distribution. Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces. Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity. They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves. Overall, the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness. The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures. Here, an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks. The method was based on the matched reservoir interval density, reservoir interval density, and matched sweet spot density of bedding-parallel fractures. The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data. The average relative prediction error was 8% in the upper sweet spot in the Lucaogou Formation, indicating that the evaluation parameters for bedding-parallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods. The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks, providing reliable technological support for the exploration and development of hydrocarbons.

Intelligent seismic AVO inversion method for brittleness index of shale oil reservoirs

The brittleness index (BI) is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development. Seismic amplitude variation with offset (AVO) inversion is commonly used to obtain the BI. Traditionally, velocity, density, and other parameters are firstly inverted, and the BI is then calculated, which often leads to accumulated errors. Moreover, due to the limited of well-log data in field work areas, AVO inversion typically faces the challenge of limited information, resulting in not high accuracy of BI derived by existing AVO inversion methods. To address these issues, we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients. Based on this, an intelligent AVO inversion method, which combines the advantages of traditional and intelligent approaches, for directly obtaining the BI is proposed. A TransU-net model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI. By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples, the challenge of limited samples is effectively addressed, and the direct inversion of the BI is stably achieved. Tests on model data and applications on field data demonstrate the feasibility, advancement, and practicality of the proposed method.

Scaling-up dynamic elastic logs to pseudo-static elastic moduli of rocks using a wellbore stability analysis approach in the Marun oilfield, SW Iran

Wellbore stability analysis is a critical component of petroleum engineering, evaluating the risks of sanding, reservoir compaction, and casing failures. Laboratory rock mechanical measurements must be scaled up to reservoir scales to achieve accurate results. One challenge lies in upscaling dynamic measurements from petrophysical logs to pseudo-static elastic properties, which has significant implications for oil and gas operations. We present a novel approach that combines laboratory rock mechanical measurements with well-log data to develop a mechanical earth model (MEM) for an Iranian oilfield with over 350 wells. We conducted static elastic property measurements on 40 core samples from various layers and depths of carbonate and sandstone rocks, demonstrating the practical application of our approach. By integrating these measurements with dynamic log data and static-dynamic correlations, we established a framework for evaluating the mechanical properties of different layers. Our findings indicate that the safe mud weight window ranges from 41.5 to 118.59 pcf, while the stable mud weight window ranges from 41.5 to 156 pcf. We demonstrate the importance of conducting parallel rock mechanical studies on cores and logs to reduce uncertainties, costs, and risks during oil and gas operations. We also propose a novel methodology combining lithological characteristics, abnormally high pressure, and borehole instability mechanisms to evaluate the stability of borehole walls. This framework provides a fresh perspective on wellbore stability analysis and offers practical solutions for the industry. Essential novel techniques include developing a geomechanical model that integrates laboratory rock mechanical measurements with well-log data to evaluate mechanical properties and calculate safe and stable mud-weight windows. Our study advances wellbore stability analysis by providing a new method for addressing this long-standing challenge. It offers valuable insights for petroleum engineers working in the oil and gas industry.

Increase in porosity and permeability resolution for thin-bedded Miocene formation in Carpathian Foredeep using different clustering methods

AbstractThe accurate interpretation of well-logging data is a crucial stage in the exploration of gas- and oil-bearing reservoirs. Geological formations, such as the Miocene deposits, present many challenges related to thin layers, whose thickness is often less than the measurement resolution. This research emphasizes the potential of utilizing electrofacies in such challenging environments. The application of electrofacies not only allows for the grouping of intervals with similar physical characteristics but can also be useful for estimating porosity and permeability parameters. For this purpose, various clustering methods were tested, including the 2D indexed and probabilized self-organizing map (IPSOM) method with and without supervision. Subsequently, the usefulness of the obtained results to improve the estimation of porosity and permeability parameters with the help of artificial neural networks was verified. As a result of the conducted analyses, significantly better results were obtained compared to classical petrophysical interpretation. The calculated porosity and permeability parameters were characterized by much greater variability and alignment with laboratory measurements on porosity and permeability. The best results were obtained for the IPSOM method, but the other methods did not differ significantly. In conclusion, the studies have shown a positive result of applying clustering methods, including the IPSOM method, to improve the estimation of permeability and porosity parameters in complicated, thinly-layered formations.

An interpretable ensemble machine-learning workflow for permeability predictions in tight sandstone reservoirs using logging data

Tight sandstone reservoirs exhibit strong vertical heterogeneity and complex pore structures, challenging conventional permeability evaluation methods based on well-logging data. Although rising machine-learning (ML) techniques have demonstrated excellent accuracy for industrial applications, the physics and rationality within such a powerful “black box” remain less clear. Hence, reliable permeability prediction would benefit from an interpretable ML-based workflow that could reveal the controlling factors. To compare the models and examine the underlying features, 16 different ML submodels are tested after data preprocessing, feature selection, and hyperparameter optimization. By comparing the fitting accuracy and tuning time, the light gradient boosting machine optimized by the whale optimization algorithm, referred to as LGB-WOA, is determined to be the optimal model with the best fitting accuracy and relatively short tuning time. A field data application demonstrates that even in highly heterogeneous reservoir sections, the LGB-WOA model outperformed conventional petrophysical models by being the most consistent with reservoir permeability directly measured from the core samples ([Formula: see text]). The Shapley additive explanation values are then used to interpret the predictions of our LGB-WOA model. As expected, the porosity curve exhibits the highest feature importance among all input features, significantly contributing to permeability predictions. Conversely, a wellbore diameter and compensated neutron log contribute the least and need not be used for subsequent model improvements. These experiments and workflow provide a powerful method for accurately assessing the permeability in complex reservoirs and contribute to a broader understanding of the application of ML in reservoir characterization, paving the way for establishing more interpretable and reliable prediction models.

Spatiotemporal distribution of sediment waves in the northern Gulf of Mexico Basin, USA

The northern Gulf of Mexico basin, known for its hydrocarbon potential and a myriad of geologic structures and processes, has been underexplored to understand deepwater sediment waves. The recent release of a vast amount of both 2D and 3D seismic data by the Bureau of Ocean Energy Management (BOEM) calls for a basin-wide identification of the sediment waves in the Gulf of Mexico.Integrating seismic, well-log, and high-resolution bathymetric data, this study identified sediment-wave fields on the seafloor as well as in the stratigraphic record. These sediment waves have an average wavelength of 798 m and an average wave height of 18 m. On the present-day seafloor, sediment waves are only located on the northwestern continental slope and eastward of the Bryant Fan area (south of Green Knoll). However, in the stratigraphic record, these bedform structures were found to be prevalent across the northwestern continental slope, northeastern continental slope, and continental rise. All of these sediment waves migrate upslope with crests that are perpendicular to the direction of basin-slope (i.e., parallel to the bathymetric contours). Thus, they are interpreted as cyclic-steps bedforms produced by supercritical sediment gravity flow processes. Investigations of these sediment waves have implications for petroleum geology, geohazard studies, oceanography, hydrodynamics, paleoclimate, and coastal engineering.

2D Seismic Structural Study of the Area Between Halfaya, Noor and Amara Oil Fields, Southeastern Iraq

The study area is located in the Missan government in southeastern Iraq, including oilfields (Halfaya, Noor, and Amara). The study area is about 966 Km2. Based on the 2D seismic reflection interpretation, well-logs data (sonic and estimated density logs), and synthetic seismogram of well Am-2, two horizons were identified and picked (Nahr Umr and Shuaiba) within the Lower Cretaceous age. For the picked reflectors, two-way time and depth maps were created to display the subsurface's structural makeup. The structural interpretation shows two structural axes in the study area. The first is the Halfaya-Amara axis, whose direction is WNW-ESE corresponding with the Amara structure. This axis deviates southeast due to the tectonic effect at the Halfaya structure. Another tectonic force affected the Halfaya structure in the northwest-southeast direction leading to the appearance of a branch of a convex extension towards the north from the middle of the Halfaya structure, completing this branch to a structural closure as nose towards the Noor structure in the northern part of the study area. The second axis of Halfaya-Noor oilfields trending northwest-southeast direction.

Structural assessment and petrophysical evaluation of the pre-Cenomanian Nubian sandstone in the October Oil Field, central Gulf of Suez, Egypt

The low quality of the seismic resolution of the studied Cambrian Pre-Cenomanian formations, which rest directly on the basement in the October Field in the Gulf of Suez, requires study, and the structure pattern controlling these formations is uncertain. The poor quality of the seismic data resolution in the Gulf of Suez is due to the presence of thick sequences of evaporites represented by the South Gharib and Zeit formations. Therefore, the current study used seismic reflection data integrated with well-log data to define the structural-stratigraphic setting of the pre-Cretaceous Nubian reservoir sequence. The study aims at locating and delineating zones having excellent reservoir potential within the Nubian sequence horizons (Nubian Transition, main Nubian, Nubian marker-1, and Nubia marker-2, referred to as NT, MN, M1 and M2, respectively) through the integration of the 3D static modelling and the petrophysical parameters deduced from the well-log data processing. A detailed petrophysical analysis, based on the wireline logs of some wells, was performed to determine the lithological units using two types of cross plots (Neutron-Density and M-N plots), and the different petrophysical parameters that characterize the various Nubian horizons were also computed. 3D modelling was used to visualise the lateral and vertical changes of the reservoir characteristics. The seismic mapping reveals that the Nubian structure is controlled by an NW-SE trending (Clysmic trend) of an NE-plunging asymmetrical anticlinal feature unconformably resting on a basement horst. The main structure is affected by two major normal faults, which trend NW, andis cut by two smaller sub-parallel faults that disturb the core of the structure, which is unaffected by the easterly oriented faults of the Aqaba trend. The petrophysical analyses indicate that the Nubian sandstones have intervals of good reservoir quality, with a total net-pay thickness of about 500 m, net sand ratios (60–90 %), effective porosity (10–25 %), and hydrocarbon saturation (85%), which may encourage further drilling, especially in the southeast portion. The result of this study can be extended to the other Nubia sandstones resting on the basement complex in the Gulf of Suez.

P-Wave Velocity Log Simulation for Petroelastic Inversion Using Gardner’s Equation, Neural Networks And Ensemble Methods Based on Trees: A Case Study of the Santos Basin, Brazil

One of the main tools for reservoir characterization is analyzing well-log data. The importance of such methods stems from petrophysical properties estimation, such as porosity, which is very important to the oil and gas industry. In scenarios where data is hard to collect, data loss and technical failures during the acquisition impose an extra challenge. Thus mathematical and petrophysical models are good candidates to fill information gaps in the well-log dataset. In such a way, the rock’s petroelastic and petrophysical properties can be successfully estimated. Several studies correlate the velocity of compressional waves (VP) to other basic well data. In this study, we used the Gardner equation and Machine Learning methods such as Neural Networks, Random Forest and Gradient Boosting regressions to generate VPlogs. We used real-world data acquired from twenty wells of the pre-salt formation from Santos Basin in Brazil to train and test the Machine Learning methods and evaluated the data estimated by those models using statistical metrics. We calculated the acoustic impedance from the estimated logs and used it to create a prior model for a petroelastic inversion, which allowed us to estimate the natural logarithm of the acoustic impedance for a seismic volume. The Machine Learning methods presented lesser errors between estimated and measured velocities when compared to Gardner’s equation.

Paleosol-induced early dolomitization with U[sbnd]Pb age constraints and its implications for fluid pathways in ancient sandstone aquifers

In hydrogeology, a key challenge involves identifying patterns within ancient formations to forecast the distribution of fluid pathways and barriers to permeability, as well as comprehending the palaeohydrological system and its changes over time. This study addresses two main research inquiries concerning fluid flow: firstly, the influence of dolomitization induced by paleosols on flow characteristics, and secondly, the implications for fluid flow pattern distribution in continental sedimentary units. The objectives are pursued through: (1) meticulous, high-resolution measurements of porosity and permeability coupled with well-log data from an outcrop and two boreholes; (2) investigation of petrographic features of diagenetic minerals and their ages using the UPb geochronology system; and (3) an integration of these methodologies to grasp rock properties and fluid flow at a broader scale. Findings suggest that early dolomitization in continental sequences significantly affects fluid flow properties across the basin. The development of paleosols triggered early dolomitization, supported by UPb geochronology evidence. Subsequent groundwater migration along hydraulic gradients, influenced by fluctuations in the local aquifer's water table, facilitated the vertical distribution of dolomitization. Dolomitization occurred in residual pores resulting from initial mineral alteration, early lithifying the sediment and preventing mechanical compaction, thus preserving porosity. During migration events, fluids moved vertically along local faults and horizontally through the dolomitized layers of the formation, which likely remained porous at the time, leading to substantial silica mineralization.