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.

Understanding the failure mechanisms affecting cement sheath integrity under ultra-high temperature (UHT) conditions is essential for ensuring wellbore integrity and supporting the safe and sustainable extraction of deep oil and gas resources. However, difficulties in characterizing moisture migration have limited understanding of the physicochemical mechanisms and cementing processes that drive failure of cementing materials in such environments. To address these challenges and enhance understanding of cement sheath failure mechanisms, this study systematically evaluates the adaptability and advantages of Neutron-X-ray Dual-modal Imaging (NXDI) technology. NXDI's extensive applications in materials science, mineralogy, civil engineering, and energy research provide representative evidence of its potential. Building on this foundation, the study proposes, for the first time, a conceptual framework, technical roadmap, and future prospects for applying NXDI to cement sheath research under UHT conditions. Our findings indicate that intensified moisture migration critically affects the microstructure and physicochemical stability of cement sheaths. NXDI offers unique potential to overcome the limitations of conventional imaging methods by simultaneously characterizing moisture dynamics and microstructural evolution. Nevertheless, its application still faces challenges, including neutron imaging quality, dual-modal image fusion, sample representativeness, and practical implementation. To address these, we outline key research priorities and feasible steps. With continued advances in imaging technologies and experimental methods, NXDI is expected to play an increasingly important role in enhancing the efficiency, safety, and sustainability of deep oil and gas extraction.

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.

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.

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.

Carbonate minerals (calcite and dolomite) and pyrite are important brittle minerals in organic-rich shales; however, the mechanism of their formation is still uncertain. This study examined the formation of pyrite and carbonate minerals (mainly calcite and dolomite) in the shales of Longmaxi (LMX) Formation through a combination of petrological and geochemical techniques. The results demonstrated that two types of carbonate minerals (calcite and dolomite) and multiple-types of pyrite were identified in the studied shales. The carbonate minerals comprise (1) subhedral to euhedral calcite and dolomite with large diameters (>10 μm), along with low Fe (<2%) and Mn (<0.2%) contents, and (2) calcite and dolomite cement, along with high Fe (>2%) and Mn (>0.2%) contents. The pyrite is primarily composed of framboidal pyrite, infilled framboidal pyrite, pyritic masses, and euhedral pyrite. This study suggests that depositional and diagenetic processes had significant influences on the formation of calcite-dolomite-pyrite in organic-rich shales. In the synsedimentary stage, the expansion of anoxic seawater contributed to the enrichment of organic matter in the shales of lower LMX Formation. In the early diagenesis stage, both the microbial sulfate reduction (MSR) and sulfate-driven anaerobic oxidation of methane (SD-AOM) would generate abundant CO₂, which together with the consumption of further contribute to the precipitation of carbonate minerals (calcite and dolomite). In addition, the MSR would also release ³²S-enriched H₂S and result in the formation of framboidal pyrite/infilled framboids/pyritic masses with low δ³⁴S values (<0‰). However, the SD-AOM would result in the precipitation of euhedral pyrite with high δ³⁴S values (>20‰) and high Co contents (>700 μg/g). In the late diagenesis stage, carbonate cements with high Fe and Mn contents formed due to the diagenetic fluids (high Mg²⁺, Fe²⁺, Mn²⁺ concentrations). Overall, the sulfate-driven anaerobic oxidation of methane (AOM) during early diagenesis in sediments constitutes a key process influencing methane release, in which case extensive methane release during this period may have contributed to Early Silurian global warming.

This study explores the depositional and diagenetic heterogeneities of the Sarvak Formation, a paleokarst carbonate reservoir in the Zagros Basin, Iran, using sedimentological, diagenetic, and statistical analyses. Petrographic studies and well log data, combined with methods like Winland rock type classification, hydraulic flow unit analysis, and Lorenz zonation, were used to assess reservoir properties at multiple scales. Two heterogeneity scales were identified: small-scale (millimeter to centimeter), driven by facies variations and diagenesis, and large-scale (meter), linked to facies transitions and sequence stratigraphy. Large-scale heterogeneities, particularly those tied to sequence stratigraphic boundaries, are traceable within third-order sequences and systems tracts. The highest-quality reservoir intervals, characterized by rudist-rich zones and dissolved shoal facies, are found beneath the Cenomanian-Turonian boundary, preserving youthful karst features. In contrast, non-reservoir intervals beneath the mid-Turonian disconformity exhibit collapsed features from prolonged subaerial exposure, forming an over-mature karst profile. Small-scale heterogeneities require further investigation within lower-order sequences for accurate prediction. This research integrates various analytical methods, providing a robust framework for understanding how depositional and diagenetic factors control reservoir quality, enhancing predictability in karstified carbonate systems for hydrocarbon exploration and development.

Surfactant flooding is one of the widely applied enhanced oil recovery methods. However, the application of a single surfactant is often insufficient to meet the demand for oil recovery. Consequently, a variety of surfactant mixtures have been employed to enhance oil recovery efficiency. A comprehensive understanding of the interactions between surfactant mixtures, crude oil, formation water and rock is essential for the effective design and optimization of the surfactant flooding procedures. In this work, anionic-zwitterionic and anionic-nonionic surfactant mixtures with varying compositions were investigated using different microfluidic devices. Single dead-end pore structure that was captured from a real rock pore throat structure was initially employed for a quick screening of the optimum surfactant composition, considering the effect of the presence of connate brine. The selected surfactant compositions were tested using a micromodel with heterogeneous pore-throat network. The results indicate that 0.4 wt% sodium dodecyl benzene sulfonate with 0.6 wt% octadecyl dimethyl betaine and 0.4 wt% sodium dodecyl benzene sulfonate with 0.6 wt% hexyl D-glucoside surfactant mixtures resulted in higher recovery rate in the dead-end pore structure. In the subsequent micromodel experiments, 0.4 wt% sodium dodecyl benzene sulfonate with 0.6 wt% octadecyl dimethyl betaine showed better oil recovery performance. To further explain the oil recovery performance of the surfactant mixtures, interfacial tension, viscoelasticity and critical micelle concentration values were measured and calculated to provide an insight into their interfacial activities. It was observed that anionic-zwitterionic and anionic-nonionic surfactant mixtures displaced oil in different manners attributing to their distinct interfacial properties. The anionic-zwitterionic surfactant mixture primarily displaced the crude oil through solubilization and emulsification due to its lower interfacial tension and critical micelle concentration of the surfactant mixture, whereas the anionic-nonionic surfactant mixture displaced oil through peeling and mobilization as a result of relatively higher interfacial tension and interfacial viscoelasticity.

Mathematical techniques continue to become more advanced in developing more complex solutions in different fields, especially in oil reservoir simulation, where various physical parameters are still unknown. Studying different simulation cases that improve predictability is required to comprehend the wellbore behavior under production properly. This article proposes a new analysis method that utilizes the first-order asymptotic expansion method to address single-phase oil flow in multilayered reservoirs with permeability-pressure dependent and source terms. The classical techniques, like pseudo-pressure functions, although offering small linearization of the diffusivity equation, are found to be insufficient and thus require more efficient techniques like the Perturbation Method. The proposed model allows detailed analysis of different reservoir properties and pressure-dependent permeability curves, therefore allowing for more effective optimization and understanding of the flow behavior in complex geological conditions.

Shale reservoirs are characterized by low permeability and a weak capacity for natural seepage. These reservoirs are commonly developed using a combination of horizontal well drilling and fracturing. The production performance of shale oil wells is evaluated through single-well production decline analysis, with the estimated ultimate recovery (EUR) used as an important indicator of the development outcome. This study predicted the EUR of the X233 well zone in the Qingcheng Shale Oil Field in the Ordos Basin. It reviewed the applicability of production forecasting methods to unconventional reservoirs and put forward a correlation-based approach to EUR prediction. The latter was developed by integrating the characteristics of Qingcheng shale oil production with EURs predicted using empirical methods intended for typical wells. The correlation-based approach was also designed on the basis of the relationship between cumulative oil production and the empirically derived EURs. The results suggest that the proposed method rapidly predicts the EUR of a single well using cumulative oil production from the first one, two, and three years of production. The predictions closely align with the empirically derived values, demonstrating the strong practical effectiveness of the approach put forward in this work. To determine its applicability and limitations, this study examined the EUR distribution characteristics of different well classes (Classes I to III). This research provides a simple and effective approach to rapidly predicting the EURs of shale oil wells, significantly reducing computational time and labor costs.