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 wel-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.

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.

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.

Layering is common in hydrocarbon reservoirs due to stratified sedimentary deposition, with each layer exhibits distinct characteristics. Multilayer reservoirs are typically clasifiedas systems with interlayer/formation crosflow and systems without interlayer crosflow (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 specificaly 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 crosflow 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.

The capacitance–resistance model (CRM) is a widely-used model for predicting well production rates because it requires the estimation of only a small number of parameters to describe production wells and the connectivity between injection and production wells. However, CRM parameters are not inherently spatially constrained, which can lead to unnatural results. In this research, we conducted spatially continuous regularization of CRM parameters and investigated how it affected model accuracy. A related goal was to investigate how this regularization improved the spatial interpolation of CRM coefficients. For regularization, we considered CRM parameters as values for a spatially continuous function, implemented this function using a neural network, and fited it using production history data. We employed two benchmark datasets—the egg and Costa datasets—to compare two models: the unconstrained conventional CRM and the proposed spatially regularized CRM. We concluded that the spatially regularized CRM yielded accuracy close to that of the conventional CRM; however, it provided a more interpretable spatial distribution of the CRM parameters.

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 fluidby 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.

Oil reservoirs subjected to a gas recovery technique are commonly challenged by early gas break-throughs affecting the production rate. Foam is injected to block and divert gas to reservoir sections, however, achieving this mechanism requires stable foam to withstand extreme reservoir conditions, such as temperature, pressure and salinity. In this work, synergy actions between amino-propyltriethoxysilane doped SiO₂ nanoparticles (NPs) and MFomax were investigated on rheology, interfacial tension (IFT), wettability, foam stability and quality to reduce gas mobility and enhance oil recovery (EOR). The formulation was guided by optimization, and the foam studies were carried out at optimum concentrations. Core-flood equipment was used to assess the gas mobility reduction factor (MRF) and EOR. From the findings, the foamability of the nanofluidis primarily governed by synergy action between the NPs and MFomax due to significant R² value (0.9; p < 0.05). Presence of NPs in the formulation resulted in good fluid properties such viscosity and IFT. The nanofoam stability has improved tremendously to 119% relative to MFomax foam. According to the IFT and contact angle, the detachment energy of the NPs (1.4 × 10⁸ eV) is higher than 1 eV suggesting strong adsorption at aqueous interface leading to foam stability. The nanofoam reached a maximum of 50 MRF, while MFomax was below 40 MRF. Subsequently, all the foam descended to lower quality regions around 5 PVI due to the change in foam morphology. Furthermore, the EOR recorded by nanofoam demonstrates a 7% increase on top of MFomax recovery factor. Thus, it can be deduced that the synergy of SiO₂ NPs with MFomax offer several benefits in improving the foam stability, quality, MRF and EOR.

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 nanofluid— 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-asisted ultrasonic treatment as a promising technique for upgrading heavy crude oil and enhancing its refining and transportation potential.

Produced water is the largest waste stream generated during oil and gas production, presenting significant environmental risks due to its complex chemical composition. Characterizing the contaminant profile of produced water is crucial for assessing its environmental impact, ensuring compliance with discharge standards, and developing effective pollution mitigation strategies. This study characterizes untreated produced water from Angolan offshore Block 15 over a 50-day sampling period, analyzing over 30 target constituents. Results reveal substantial variability in key parameters, including chemical oxygen demand (394.02–526.00 mg/L), biochemical oxygen demand (9.80–20.40 mg/L), phenol (2.70–4.12 mg/L), benzene, toluene, ethylbenzene and xylene (BTEX, 2.77–7.00 mg/L), polyaromatic hydrocarbons (0.20–1.20 mg/L), total organic compounds (248–918.45 mg/L), total oil and grease (19–206 mg/L), and total petroleum hydrocarbons (12–105.02 mg/L). Metals and anions exhibited significant fluctuations, with sodium ranging from 1167.77 to 2100.00 mg/L and chloride from 400 to 2146.84 mg/L. The biodegradability index (0.028) indicates limited potential for biological treatment due to toxic and refractory organics. The results emphasize the necessity for advanced treatment technologies and stricter environmental regulations to reduce contaminant discharge and minimize the ecological footprint of offshore oil production.

The Paleocene marked a critical structural transition period from tectonic subduction to extensional rifting in the Bohai Bay Basin. To reconstruct the spatiotemporal structural framework, evolutionary process, and basin prototype during the Paleocene Kongdian period of the Dongying Depression in the eastern Bohai Bay Basin, this study integrated seismic interpretation, core observation, ⁴⁰Ar/³⁹Ar dating, and structural restoration. The ⁴⁰Ar/³⁹Ar dating of basalts (approximately 57.58 ± 0.77 Ma) revealed that the depression entered an active rifting stage with intense volcanism in the early Paleocene Kongdian period. Regional faults, including the Chennan, Wangjiagang Shicun, and Gaocheng faults, were well developed during the first member of the Kongdian sedimentary stage. Their activity indicates rapid rift expansion and resulted in a half-graben pattern characterized by faulting in the north and onlap in the south. Structural restoration showed that the stretching ratio during the Paleocene Kongdian period was approximately 7.3%, which reflects rapid rifting. The development of the early Paleocene rift has important implications for deep hydrocarbon exploration in the Bohai Bay Basin.