Layering is common in hydrocarbon reservoirs due to stratified sedimentary deposition, with each layer exhibits distinct characteristics. Multilayer reservoirs are typically classified as systems with interlayer/formation crossflow and systems without interlayer crossflow (commingled systems). This study focuses on the latter, where fluid exchange between layers occurs solely through the wellbore. Accurately interpreting pressure transient data from commingled systems to extract individual layer properties remains a persistent challenge. Most existing interpretation techniques rely on assumed reservoir models (typically homogeneous, isotropic, and infinite in extent) that involve numerous unknown parameters (such as permeabilities and skin factors) and require nonlinear history matching. These assumptions not only introduce subjectivity but can also yield unreliable estimates. The most reliable way to characterize individual layers has traditionally required isolating and testing each layer separately, which is a process that is both technically complex and costly to implement. This study introduces a testing and analysis methodology developed specifically for commingled multilayer reservoirs. The proposed approach utilizes deconvolution to remove rate variation effects from bottomhole pressure data and generate pressure responses equivalent to constant-rate conditions. This transformation enables the recovery of distinct pressure signatures for each individual layer without requiring assumptions. The recovered pressure signals are wellbore storage (WBS) free and extend over the entire test duration, offering a clearer window into the dynamic behavior of each contributing formation. Deconvolution needs complete sandface rate data for each layer, data that are typically unavailable in field practice. To address this, the study develops a simple yet effective power-law model that reconstructs layer-specific rate profiles using only a few discrete production logging tool (PLT) measurements, making the overall approach feasible for real-world applications. The method was implemented and validated on three simulated oil reservoir scenarios, each exhibiting different reservoir/boundary characteristics. The results show that the estimated rate profiles, when input into a stable deconvolution algorithm, produce layer-specific pressure responses. The technique successfully distinguishes the flow behavior and boundary conditions of each individual layer, even under complex conditions such as heterogeneity, no-flow boundaries, dual-porosity formations, and wellbore crossflow during shut-in periods. This advancement enables efficient reservoir description and production optimization, by eliminating the need for mechanical isolation or extended shut-in periods, offering a practical alternative that is aligned with the industry demands for minimum well intervention and cost-effective surveillance.

Accurate reservoir characterization is essential for optimizing hydrocarbon exploration and production, particularly in complex deep-water environments. The Simsat Field, Offshore Nile Delta, features a Pliocene deep-water turbidite reservoir, primarily composed of interbedded sand channels and shale, which poses significant challenges for reservoir delineation due to lateral lithological variations, limited well control, and complex stratigraphic architecture. Traditional seismic interpretation methods often struggle to capture the heterogeneity and connectivity of these reservoirs, leading to uncertainties in hydrocarbon prospect evaluation. To address these challenges, this study integrates seismic attributes, post-stack seismic inversion, and a multi-layer feed-forward neural network (MLFN) to enhance quantitative reservoir characterization. This integrated approach outperforms the limitations of individual techniques by combining the spatial resolution of seismic attributes, the lithology-fluid sensitivity of inversion, and the non-linear predictive capabilities of machine learning. The workflow provides a synergistic solution that improves property prediction accuracy and reduces interpretation uncertainty, particularly in data-limited, structurally complex settings. Spectral decomposition improves the visualization of channel morphology and stratigraphic variations, while seismic inversion generates acoustic impedance volumes that aid in lithology differentiation and fluid detection. The MLFN model, trained using well log data and multiple seismic attributes, provides high-accuracy predictions of shale volume (Vsh), porosity, and water saturation, significantly improving the assessment of reservoir quality. The results confirm a well-defined gas-bearing sandstone reservoir with high porosity (>18%) and low water saturation, indicating strong hydrocarbon potential. This integrated approach demonstrates the effectiveness of combining advanced seismic interpretation with machine learning techniques to reduce interpretation uncertainties, improve reservoir connectivity analysis, and optimize field development strategies in the West Delta Deep Marine (WDDM) concession. The findings provide valuable insights into reservoir heterogeneity and contribute to more effective hydrocarbon exploration and production in the Offshore Nile Delta.

This study uses 13 proprietary data sets from sedimentary basins around the globe to constrain the permeability and porosity ranges occupied by clastic rocks (sandstones and shale). The combined data sets represent a total sample size of 21,767 data pairs of measured porosity and permeability. The data are combined in various ways and analyzed in considerable detail, in what is presently assumed the most comprehensive permeability analysis of clastic data sets. First, comprehensive porosity-permeability transforms are plotted with the aim to understand how well the two quantities actually correlate, and what may be the root cause(s) of the huge variation in their correlation. The analysis of the empirical data is embedded in a review of prior work related to permeability transforms. The Kozeny-Carman relationship is revisited and a modified scaling approach is proposed. First, it is shown how hydraulics of pore tubes of various shapes and tortuosity relate to macroscopic permeability. The key scaling factor, called here the permeability-reduction factor, β/τ, is the ratio of the coefficient of hydraulically effective pore space, β, and the tortuosity, τ. Whereas it is concluded that the porosity is a very poor predictor of the permeability, there appears to exist a close relationship between the permeability-reduction factor and the permeability, confirming the fundamental physical nature of β/τ as an excellent predictor of hydraulic transmissibility in porous media made up of sedimentary mineral aggregates. The inferred relationship provides the basis for a new method to construct permeability transforms, from bootstrapped data sets, using Monte-Carlo simulation.

Cellulose nanoparticles are attracting interest in diverse fields because of their availability, low cost, and benign nature. Albeit previous studies have reported excellent experimental results, the application of cellulose nanoparticles in oilfield remains a challenge. Therefore, the objective of this research is to provide vital information on how to design cellulose nanoparticles for oilfield applications. Herein, the sources of cellulose and their derivatives were presented. Subsequently, cellulose pretreatment and extraction methods were elucidated. Likewise, the design of cellulose nanoparticles for oilfield applications were discussed. Also, the enhanced oil recovery (EOR) mechanisms of cellulose nanoparticles for high temperature high pressure (HTHP) reservoirs were identified and their application in EOR was reviewed. The challenges hindering full scale field application of cellulose nanoparticles were presented while concurrently shedding light on diverse methodologies employed to maintain their stability in HTHP oil reservoirs. The results indicate that wettability alteration, asphaltene precipitation, interfacial tension and viscosity reduction are the dominant EOR mechanisms by cellulose bionanomaterials, it also demonstrate that cellulose bionanomaterials can increase the viscosity of injected fluid by 10-100%, improve sweep efficiency by 52-98% and improve oil recovery by 10-35% original oil in place. It can be concluded that bionanomaterial have potential for oil field applications and are economically viable due to their high recovery rate.

The Cretaceous Sarvak Formation in the Zagros Basin exhibits complex reservoir heterogeneity controlled by depositional facies and sequence stratigraphy-constrained diagenesis. This study integrates petrographic and petrophysical analyses of an Abadan Plain oilfield to establish predictive relationships between microfacies, diagenesis, and automated hydraulic flow units (HFUs). Eight microfacies were identified, representing four depositional environments: open marine, shoal complex, patch-reefs, and lagoon, supporting a ramp model. Reservoir quality varies systematically across four HFU classes, ranging from poor (HFU1: 0-0.1 FZI, 2.76 mD permeability, 10.85% porosity) to excellent (HFU4: >0.7 FZI, 45.99 mD permeability, 17.4% porosity). Diagenetic processes show distinct relationships with sequence stratigraphy: marine cementation dominates transgressive systems tracts, while meteoric dissolution and fracturing prevail in regressive systems tracts and at sequence boundaries. Maximum flooding surfaces record chemical compaction and authigenic mineralization. Facies-selective diagenesis primarily controls reservoir quality. Grain-supported facies develop dissolution-enhanced porosity, whereas mud-dominated facies experience porosity reduction through cementation and compaction. Dolomitization and fracturing provide secondary permeability enhancement, particularly in mid-ramp settings. HFU classification reveals optimal reservoir performance in zones where dissolution and fracturing enhance pore networks (HFU3-HFU4). In contrast, non-reservoir zones (HFU1-HFU2) are affected by extensive cementation and compaction. These findings highlight the predictive potential of integrating sequence stratigraphy with diagenetic analysis for reservoir characterization in carbonate systems. The established facies-to-diagenesis relationships provide a robust framework for understanding reservoir heterogeneity and fluid flow behavior in the Sarvak Formation and analogous carbonate reservoirs.

Gas injection is frequently used to displace the trapped oil. However, this may lead to deposition of asphaltene, which can profoundly block the pores and throats of the porous media, resulting in reduced porosity and permeability. The rock lithology, composition of the injected fluids, pressure, and temperature are among the parameters that may influence asphaltene deposition. This study investigated the asphaltene precipitation/deposition while injecting rich hydrocarbon gas into a heavy oil reservoir. The experiments were conducted under two conditions: miscible and immiscible gas injections. Two rich hydrocarbon gases with different compositions were used, and experiments were conducted under reservoir conditions. Experiments were performed in carbonate cores previously saturated with heavy live oil. The extent of formation damage was assessed by measuring asphaltene deposition during the flooding of miscible and immiscible rich hydrocarbon gases into the core plugs. The outlet oil samples from the core flooding were analyzed using molecular weight measurements, UV spectroscopy, and the IP143 method. During the miscible flooding process, where rich hydrocarbon gas was injected into live oil at 210 ℉, the molecular weight of the outlet oil samples decreased by 53%. The porosity and permeability of the carbonate core in miscible flooding at 210 ℉, after washing with cyclohexane, showed a reduction of 13.6% in porosity and 42.8% in permeability. In the course of immiscible flooding with rich hydrocarbon gas, the outlet oil at 104 ℉ decreased by 48.3% in molecular weight, 35.5% in absorption coefficient, and 30.7% in asphaltene content. The porosity and permeability of the carbonate core in immiscible flooding decreased by 11.8% and 43.25%, respectively. The results showed that both miscible and immiscible injection processes reduce the porosity and permeability of carbonate cores. The damage was particularly severe during the miscible flooding.

Upgrading heavy crude oil remains one of the most critical and technically demanding processes in the petroleum industry, primarily due to the inherently poor quality of the various types of crude oil and the complexities associated with their transport and refining. This study investigates the impact of a nano-fluid—composed of aluminum oxide nanoparticles dispersed in a kerosene solvent and stabilized with the surfactant sodium dodecylbenzenesulfonate—on enhancing the quality of crude oil extracted from an East Baghdad field. This nanofluid was applied using ultrasonic irradiation to ensure uniform dispersion and stability. The results demonstrated a substantial improvement in various crude oil properties. The viscosity was reduced significantly, from 58.15 cP to 5.58 cP, representing a 90.4% improvement. The API gravity increased markedly, from 19.63 to 30.49. Furthermore, the heavy metal content decreased considerably, with vanadium reduced from 109.67 ppm to 9.02 ppm (92% reduction) and the nickel content showing an 85% decrease. The sulfur content also declined sharply, from 4.422% to 0.77%, indicating an 83% improvement. These findings confirm the high efficacy of nanofluid-assisted ultrasonic treatment as a promising technique for upgrading heavy crude oil and enhancing its refining and transportation potential.

In oil and gas exploration, kick is a typical high-risk downhole incident. Currently, most intelligent kick detection methods belong to supervised learning, which depends on labeled samples and is prone to class imbalance issues in the training set, leading to reduced model accuracy and higher false alarm rates in practical applications. To address this, this paper focuses on the application of unsupervised learning in kick detection, proposing a kick detection method based on Long Short-Term Memory Autoencoder (LSTM-AE) combined with parameter trend change rules. The study integrates LSTM-AE with expert knowledge to develop an intelligent kick early warning system suitable for well sites, validated using field data from five wells. The results show that the average reconstruction mean squared error of the LSTM-AE is 0.017, with an average false alarm rate of 4.56% for kick detection, detecting kicks an average of 9.2 min earlier than manual detection. This outcome confirms that kick detection can utilize unsupervised learning methods, avoiding relying on labeled samples and class imbalance issues in the training set, thereby effectively improving model accuracy and generalization ability. Moreover, the proposed LSTM-AE demonstrates superior performance in kick detection, and the detection method combining the model and judgment rules has significant implications for monitoring and early warning of other types of downhole incidents.

This study provides a comprehensive assessment of the shale gas potential of the Middle Jurassic Khatatba Formation in the Obaiyed Field, Western Desert, Egypt, by integrating geological, petrophysical, geochemical, and geomechanical datasets into a unified workflow. The multidisciplinary approach allows for the identification of shale gas "sweet spots" and addresses critical operational risks associated with drilling and completion. Petrophysical evaluation indicates heterogeneity, with total porosity ranging from 6 to 12%, effective porosity between 4 and 8%, shale volume often exceeding 40% but locally decreasing below 30%, and permeability within 0.01-0.1 mD. Gas storage capacity is enhanced at depths greater than 3800 m, where free gas contents are significant and adsorbed gas reaches 1.5-2.0 cm³/g in Total Organic Carbon (TOC)-rich intervals. Geochemical analysis confirms that the Upper Safa Member is thermally mature within the gas window, with TOC averaging ~4 wt% and mixed Type II-III kerogens, while traces of CO₂ (0.8-2.0 mol%) and H₂S (25-80 ppm) raise concerns about casing corrosion and necessitate careful material selection. Geomechanical results reveal brittle intervals with high Young's modulus and low Poisson's ratio, fracture gradients ranging from 25 to 30 MPa/km, and maximum horizontal stress (σHmax) typically 1.2-1.5 times the minimum horizontal stress (σhmin), defining a narrow safe mud weight window. Seismic inversion delineates TOC-rich, low-impedance intervals as optimal drilling targets, while fault-bounded compartments highlight both opportunities for hydrocarbon trapping and risks of reservoir compartmentalization. The integration of reservoir quality (RQ) and completion quality (CQ) with stress and pressure profiles enables optimized well orientation and trajectory planning, particularly recommending horizontal wells perpendicular to σHmax in the lower Upper Safa Member. This integrated evaluation confirms the Upper Safa Member as the most promising shale gas target within the Khatatba Formation and establishes a transferable workflow for unconventional reservoir development worldwide.

The integrity of welded X65 carbon steel (CS) pipelines in oil and gas systems is significantly compromised by preferential weldment corrosion (PWC) and pitting in sour conditions. This study clarifies that the effect of pre-corrosion conditioning and inorganic deposits (SiO₂ or CaCO₃) on welded CS pipelines, with SiO₂ deposits leading to the most substantial damage at weld metal (WM) and heat-affected zone (HAZ) relative to parent metal (PM). Due to localized acidification and differences in potential, corrosion rates (CRs) hierarchies are SiO₂-deposited > CaCO₃-deposited > non-deposited. Microstructural analysis reveals that deposited CS-WM has carbide redistribution and increase residual stress from the welding thermal process, with increased aggressive species and stabilized iron oxides, leading to intensified PWC. However, amine-based inhibitor CRW11 suppresses the CRs below 0.1 mmpy through chemisorbed films that disrupt microgalvanic coupling and protective corrosion product layers with inhibition efficiency (IE > 85%). CS-PM-CaCO₃ has a high pit depth (68.3 ± 2.7 μm) and propagation rate (1.2 ± 0.2 mmpy), making it the most vulnerable region to pitting relative to CS-WM-CaCO₃ (52.5 ± 1.1 μm; 0.9 ± 0.1 mmpy) and CS-HAZ-CaCO₃ (29.3 ± 0.6 μm; 0.5 ± 0.1 mmpy). This proves that CaCO₃ deposit on welded CS creates a physical barrier for the surface nucleation effect and improves anodic dissolution. Machine learning models (Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)) accurately predict IE (R² = 0.99). However, RF performs best for data without deposits (RMSE = 0.9), while DT is most appropriate model for those with SiO₂ and CaCO₃ deposits (lowest RMSE = 0.5 and 0.2). These findings provide coupled electrochemical, microstructural, and machine learning frameworks for PWC mitigation, allowing for adaptive corrosion management methods in sour service pipes and material designs optimized for topology.