Drilling fluid must fulfill various functions with a great impact on the drilling performance. Drilling fluid invasion can cause formation damage. Good quality mudcakes can prevent such damage. This research focuses on the lab techniques and performance results of testing innovative water-based drilling fluids containing nanoparticles (NPs) for minimizing formation damage at high-pressure/high-temperature (HP/HT) conditions. A couette type viscometer was used to examine the rheological properties of the drilling fluids tested in this research. Zeta potential measurements were conducted at different temperatures and concentrations to assess their stability and to investigate the role of charge potential. Indiana limestone outcrops were examined as the filter media for both static and dynamic filtration (up to 350°F and 500 psi) using a HP/HT dynamic filter press. The mudcakes were investigated using a computed-tomography (CT) scan, and Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS). Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) was used to measure the concentrations of key ions in the filtrate fluids. A significant reduction in the filtrate fluid volume was achieved when using ferric oxide NPs (-43% for 0.5 wt%) compared to that of the base fluid. However, adding silica NPs led to an increase in the filtrate volume and mudcake thickness. Increasing the NP concentration resulted in an increase in the fluid loss and mudcake thickness. The mudcakes consisted of two layers, as indicated by the CT scan analysis. 0.5 wt% was found to be the optimal NP concentration, which provides less agglomeration and a reduction in the mudcake permeability by −76.4%. At this concentration, the ICP-OES analysis showed a higher cation dissociation, which promoted the formation of a different clay platelet microstructure. At a higher NP concentration, a new layer of NPs was formed in the mudcake, which adversely affects the mudcake characteristics, as demonstrated by CT scan analysis and SEM-EDS elemental mapping. The rheological measurements indicated a good rheology at different temperatures and NP concentrations. Moreover, the NPs helped to stabilize the viscosity and yield stress at high temperatures (up to 200°F). Aging at 350°F for 16 hours showed that NP-based drilling fluids remain stable with minor changes in rheological properties. The obtained rheological data for various NPs is fitted to the classical drilling fluid rheological models to determine the best fit-model, which can then be applied to an efficient design. This research provides a comprehensive evaluation of improved water-based drilling fluids, using ferric oxide and silica NPs for HP/HT applications. The examined NPs have the potential to enhance drilling fluid properties, which provides more efficient drilling operations and less formation damage.

Against the backdrop of escalating global energy demand projected to rise by 50% within the next decade, this comprehensive review aims to evaluate and analyze the transformative potential of nanomaterials in Enhanced Oil Recovery (EOR) techniques. The study specifically examines the synergistic interactions between various nanoparticles (NPs) and chemical modifiers, while addressing the critical challenges in practical implementation. The scope encompasses a systematic assessment of different NPs types, their mechanisms of action, their optimization for field-scale applications, and the identification of research gaps that need to be addressed for industrial-scale deployment. The review methodology employs a multi-faceted analytical approach, combining systematic analysis of experimental studies with a critical evaluation of field implementation challenges. The performance of metal oxides and silica NPs was critically reviewed by examining their interactions with polymers, surfactants, and low salinity brines (LowSal) across diverse reservoir conditions. The analysis particularly focused on correlating NPs characteristics (type, concentration, size, and surface area) with their impact on chemical modifier adsorption, recovery factors, wettability alterations, and interfacial tension (IFT) modifications. Our analysis revealed that silica (SiO₂) and alumina (Al₂O₃) NPs demonstrate superior performance in EOR applications, with distinct advantages under specific reservoir conditions. SiO₂ NPs achieve 30–50% IFT reduction under optimal conditions, particularly at lower temperatures, while exhibiting excellent compatibility with existing EOR infrastructure. Al₂O₃ NPs exhibit remarkable stability across various temperature ranges, while zirconium oxide (ZrO₂) NPs show up to 40% IFT reduction at elevated temperatures, offering versatility for different reservoir types. Optimal NP concentration was identified between 0.1–0.5 wt%, with higher concentrations potentially causing adverse effects such as pore blockage and particle aggregation. The combination of NPs with surfactants or polymers demonstrated synergistic effects, with SiO₂ NPs–surfactant combinations achieving an additional 10–20% IFT reduction compared to SiO₂ NPs alone. Surface modifications and optimized salinity conditions improved NPs stability by 20–30% in high-salinity environments, addressing a key challenge in field applications. Importantly, recent field trials underscore these laboratory findings by demonstrating a near 98% increase in cumulative oil production (from 15,502 to 30,682 bbl) in a Colombian heavy-oil reservoir following Ni–Pd–doped Al₂O₃ nanofluid injection. These operations also improved API gravity (from 11.6° to 13.4°) and significantly reduced viscosity (from 39,987 cP to 11,734 cP), economically, the steam requirements and associated costs reduced with nanofluid treatments in only one field trial up to 48% cost reduction attributed to lower steam usage and enhanced production. Highlighting the practical impact and longevity of NPs-based EOR technologies in real-field settings. This review thus provides a systematic framework for selecting and optimizing NPs-based EOR systems while identifying critical research needs in scaling up laboratory successes to commercial deployment. The findings emphasize the need for long-term stability studies, cost-effective synthesis methods, and environmental impact assessments to fully harness the potential of nanotechnology in the petroleum industry.

Access to deeper oil and gas reservoirs in hostile environments necessitates improvement of existing drilling fluids. This work focuses on the lab techniques for developing, assessing and analyzing innovative water-based drilling fluids containing iron oxide (Fe2O3) and silica nanoparticles (SiO2). The fluid loss characteristics were examined both in an American Petroleum Institute (API) static filter press and in a High Temperature-High Pressure (HTHP) filter press under elevated pressures and temperatures (300 psi/250°F). A computed-tomography (CT) scan was used for deep analysis of the filter cake. Scanning Electron Microscopy (SEM) was used to analyze the morphology of the filter cake as well as to give deep insights for their microstructure, the interfacial phenomena and the interaction between bentonite particles and the nanoparticles. Inductively Coupled Plasma (ICP) mass spectrometry was used to determine the quality of the produced filtrate. Zeta potential measurements were used to assess the stability of the developed suspensions. The changes in the rheological properties of the nanofluids were measured at HT conditions using a standard Fann type viscometer. Significant modifications have been observed with the addition of nanoparticles to the base fluid of water-bentonite suspension in rheological and filtration characteristics. The rheological analysis showed an increase of the yield stress and of the gel strength as the concentration of nanoparticles was increased. Both the API static and the HTHP filter press indicated a remarkable improvement in the fluid loss and filter cake characteristics for the samples containing iron oxide nanoparticles. For samples containing silica nanopowder, there was an adverse effect on the fluid loss characteristics with minor changes in the rheological profile. The filtration efficiency was reduced with the increase of the concentration of Fe2O3 nanoparticles which was confirmed by CT scan measurements. Results revealed that 0.5% (w/w) is the optimal concentration for the Fe2O3 nanoparticles, above which they form a new layer in the filter cake that adversely affected the fluid loss and filter cake characteristics. SEM and ICP measurements confirmed this phenomenon and revealed the agglomeration effect and the smooth surface of the produced filter cakes. Zeta potential measurements at different concentrations and temperatures of the produced nanofluids showed that the iron oxide nanoparticles were stable in colloidal suspensions, whereas silica nanopowder was unstable under different temperatures. The examined nanoparticles have the potential to significantly improve the characteristics of the filter cakes at both low temperature-low pressure (LTLP) and HTHP conditions. They also have the ability to maintain optimal rheological properties so that many drilling problems can be efficiently mitigated. Their low concentration in the drilling system, compared to other conventional drilling additives, provides a basis for more efficient drilling practices.

Artificial intelligence (AI) and machine learning (ML) are transforming industries, where low-cost, big data can utilize computing power to optimize system performance. Oil and gas (O&G) fields are getting mature, where well integrity (WI) problems become more common and field operations are now more challenging. Hence, they are good candidates for transformation due to the low cost of data storage, highlighting the oil market decline, along with dynamic risk posed during operations. This paper is presenting a comprehensive compilation of different ML applications in diverse disciplines of the petroleum industry. The pool of AI and ML with respect to different areas of applications along with publication years has been categorized. The main focus of this study is classifying well integrity failures where the authors found that the potential of AI and ML in predicting well integrity failures has not been efficiently tapped, and there is an explicit gap in the literature. First, the applications of AI, ML, and data analytics in the O&G industry are discussed thoroughly, so this paper can be a comprehensive reference for readers and future researchers. Then data preprocessing is explained. This includes data gathering, cleaning, and feature engineering. Next, the different ML models are compared and discussed. Finally, model performance evaluation and best model selection are described. This study would be a concrete foundation in the design and construction of ML programs that can be deployed for WI risk management. The developed model can be simply used for any well stock, providing quick and easy assessment instead of subjective and tedious assessment. The layout can be simply adjusted to reflect the risk profile of any well type or any field.