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Accurately predicting the filtration volume (FV) in drilling fluid (DF) is crucial for avoiding drilling problems such as a stuck pipe and minimizing DF impacts on formations during drilling. Traditional FV measurement relies on human-centric experimental evaluation, which is time-consuming. Recently, machine learning (ML) proved itself as a promising approach for FV prediction. However, existing ML methods require time-consuming input variables, hindering the semi-real-time monitoring of the FV. Therefore, employing radial basis function neural network (RBFNN) and multilayer extreme learning machine (MELM) algorithms integrated with the growth optimizer (GO), predictive hybrid ML (HML) models are developed to reliably predict the FV using only two easy-to-measure input variables: drilling fluid density (FD) and Marsh funnel viscosity (MFV). A 1260-record dataset from seventeen wells drilled in two oil and gas fields (Iran) was used to evaluate the models. Results showed the superior performance of the RBFNN-GO model, achieving a root-mean-square error (RMSE) of 0.6396 mL. Overfitting index (OFI), score, dependency, and Shapley additive explanations (SHAP) analysis confirmed the superior FV prediction performance of the RBFNN-GO model. In addition, the low RMSE (0.3227 mL) of the RBFNN-NGO model on unseen data from a different well within the studied fields confirmed the strong generalizability of this rapid and novel FV prediction method. [ABSTRACT FROM AUTHOR]

Effective management of solid content in drilling fluids is crucial for minimizing operational challenges and ensuring high efficiency during drilling operations. Accurate and timely quantification of solid content remains a challenge due to the lengthy laboratory measurement techniques currently in use. As a result, existing solid content measurement methods lack real-time capabilities, hindering proactive monitoring of this parameter. This study develops integrated machine learning and optimization models to predict solid content based solely on two drilling fluid properties that can be quickly measured at the well site—fluid density and Marsh funnel viscosity. A dataset of 1290 records was compiled from drilling fluid compositions used in drilling 17 wells across various lithological formations in two fields in southwest Iran. The Least Squares Support Vector Machine and Radial Basis Function Neural Network models are integrated with Particle Swarm Optimization and Grey Wolf Optimizer to create efficient and rapid solid content prediction models. Six intelligent model configurations, including four hybrid machine learning-optimizer models, were trained and evaluated for their prediction and generalization performance. The hybrid models outperformed the standalone models, with the Least Squares Support Vector Machine-Grey Wolf Optimizer model achieving the lowest prediction error (root mean square error = 1.220 % with the testing dataset). Feature importance analysis reveals that fluid density has a more significant impact on solid content predictions than Marsh funnel viscosity. These novel two-variable hybrid models can be applied in well-site drilling fluid laboratories to enable frequent control and effective management of solid content during drilling operations. [Display omitted] • Prompt drilling fluids solid content prediction and monitoring. • Integrated machine learning optimization models suitable for well-site implementation. • Predictive models receive two inputs: fluid density and Marsh funnel viscosity. • Optimized least-squares support vector machine reliably predicts solid content. [ABSTRACT FROM AUTHOR]

Accurate and frequent monitoring of the solid content (SC) of drilling fluids is necessary to avoid the issues associated with improper solid particle concentrations. Conventional methods for determining SC, such as retort analysis, lack immediacy and are labor-intensive. This study applies machine learning (ML) techniques to develop SC predictive models using readily available data—Marsh funnel viscosity and fluid density. A dataset of 1290 data records was collected from 17 wells drilled in two oil fields located in southwest Iran. Four ML models—least squares support vector machine (LSSVM), multilayered perceptron neural network, extreme learning machine, and generalized regression neural network—were developed to predict SC from the compiled dataset. Multiple assessment techniques were applied to attentively evaluate the models’ prediction performances and select the best-performing, SC prediction model. The LSSVM model generated the least errors, exhibiting the lowest root-mean-square error values for the training (1.80%) and testing (1.84%) subsets. The narrowest confidence interval, 0.18, achieved by the LSSVM model confirmed its reliability for SC prediction. Leverage analysis revealed minimal influence of outlier data on the LSSVM model's SC prediction performance. The trained LSSVM model was further validated on unseen data from another well drilled in one of the studied oil fields, demonstrating the model’s generalizability for providing credible close-to-real-time SC predictions in the studied fields. [ABSTRACT FROM AUTHOR]

[Display omitted] • Certain synthetic polymers (SP) are highly effective cement slurry (CS) additives. • CS encounter multiple problems in deep high temperature/ high pressure (HPHT) wells. • Certain monomers can create SPs that improve CS performance in harsh conditions. • Nanocomposite polymers improve CS compressive strength and gas blockage functions. • Economic and environmental issues impact SP deployments as CS additives. • A new four-class division is proposed for polymers used as wellbore cement additives. In recent years, more deep and ultra-deep gas and oil wells have been drilled. The high-temperature and high-pressure (HPHT) conditions encountered in such wells frequently contribute to drilling problems being encountered. Sub-surface problems such as loss circulation, gas migration, fluid inflows, and wellbore wall swelling and collapse impact the quality of wellbore cementing. Such complex wellbore conditions place higher demands on cement, water, and drilling fluid admixtures. Cement slurries (CS) tend to become unstable as the thermal movements of the component increase above certain critical levels. Ultimately, thermal degradation of the CS under harsh conditions leads to the release of free fluids and hydration effects. These negatively impact CS performance, reducing its ability to generate effective fluid seals in the wellbore walls. Chemical additives are used in CS in attempts to address certain issues such as retardation, fluid loss, dispersion, settling prevention, and gas migration control. However, many additives become less effective in HPHT subsurface conditions. Recently, the involvement of synthetic polymers (SP) as CS additives has demonstrated their ability to respond more favorably to harsh sub-surface conditions. However, issues remain with SP requiring further improvements to make them more effective as CS additives in harsh conditions. Therefore, this review describes the main challenges CS faces in a wellbore: fluid loss, hydration, high salinity, HPHT, gas migration, and adverse rock formation responses. The SP additives, which are now able to overcome most of these challenges, are explained. The functions and mechanism of SP in gas and oil borehole CS, together with their benefits and drawbacks, are addressed in detail. The principal suitable monomers used to polymerize SPs for beneficial use as CS additives are identified, and their properties are compared. Moreover, a new four-category classification is proposed for polymers used as wellbore cement additives, which distinguishes SP, nano-SP, self-repairing polymers, and geopolymer materials. Economic factors and environmental impacts associated with SP use as CS additives are also identified. Based on the review's findings, recommendations are made for further research and development work required to address outstanding concerns with respect to such SP applications. [ABSTRACT FROM AUTHOR]

A type of natural resource found in nature, starch and its derivatives, homologues, and other compounds have a wide range of sources. These materials are cheap to obtain and have the advantages of low toxicity, rapid breakdown, and limited environmental footprint. However, starch's actual commercial usage remains relatively constrained, largely due to its low stability as its temperature increases, poor solubility in water, and a propensity to ferment at certain temperatures. However, certain modified forms of starch do display high-temperature tolerance that increases their attractiveness for a wider range of applications. In particular, modified-starch products are becoming more widely used in drilling fluids (DF) and certain enhanced oil recovery (EOR) techniques. Modified starch displays good biodegradability, but some bonds in the starch molecule can lead to radiation degradation under certain conditions. This results in reduced polymerization, base fracture, and even carbonization, limiting its range of application. This review characterizes different categories of modified starch, describing the history of their industrial application. In the oil and gas industry DF applications predominate to date, encouraging research efforts to seek improvements in the temperature-resistance of modified starch. The review considers the processes and economic and environmental issues involved in DF applications of modified starch. • DF filtration-loss control improved by modified starch biopolymers. • Starch modification techniques to improve its resistance to harsh conditions. • Modified starches suitable for high-pressure-high-temperature wellbore conditions. • Environmental and economic aspects monomers used for starch modification. • Research requirements identified to further improving starch as DF additives. [ABSTRACT FROM AUTHOR]

Careful design and preparation of drilling fluids with appropriate rheology and filtration properties, combined with operational monitoring, is essential for successful drilling operations. Field results reveal that most drilling-fluid problems encountered are avoidable based on prompt detection of unexpected changes in fluid rheology and filtration behavior. Drilling-fluid rheology and filtration properties are typically only checked once or twice a day, whereas other drilling-fluid properties, such as fluid density (FD), solid percentage (S%), and March funnel viscosity (MFV), tend to be monitored several times per hour. Machine learning is therefore applied to estimate rheology and filtration properties with FD, S%, and MFV as input variables. A 1160-record field dataset collected from 14 wells drilled in two oil and gas fields in southwest Iran with water-based drilling fluids is used to predict the drilling fluid's rheological and filtration characteristics. Plastic viscosity (PV), yield point (YP), and filtrate volume (FV) are the targeted prediction objectives. Of six models tested, Multilayer extreme learning machine (MELM) hybridized with the cuckoo optimization algorithm (COA) provides the best PV, YP, and FV predictions. It achieves root mean squared error (RMSE) values of 0.6357 mL (FV), 0.6086 cP (PV), and 0.6796 lb/100 ft 2 (YP). MELM-COA generates rapid and accurate estimations of rheology and filtration properties with potential for real-time monitoring during drilling operations, without recourse to time-consuming laboratory filtration and rheological tests. This work delivers, in a novel way, accurate and reliable predictions of drilling fluid filtration properties using only the more readily available FD, MFV, and S% variables as input features. • Hybrid machine learning (HML) models key water-based drilling-fluid (WBFs) properties. • Novel HML model configurations deliver precise WBF filtration predictions. • Fast and accurate predictions of plastic viscosity and yield point delivered in real time. • Cuckoo optimization algorithm (COA) provides the most effective HML control values. • Multi-layered extreme learning machine with COA provides the most accurate predictions. [ABSTRACT FROM AUTHOR]

• Drilling fluid challenges in HPHT, salt contamination and loss circulation conditions. • Polymers synthesized from certain monomers can combat harsh drilling conditions. • Performance assessments of synthetic polymers as effective drill-fluid additives. • Environmental and economic factors affecting monomers and synthetic polymers. • Future research and development work required to improve polymer performance. Conventional drilling fluid (DF) technologies commonly fail to meet many of the requirements of modern drilling under various harsh subsurface conditions, particularly those associated with high pressure and temperature (HPHT), and highly saline conditions. Such conditions have a great influence on the efficiency of DF by causing adverse changes to their properties that can increase the risk of gas kicks, stuck drill pipes, and pipe twist-offs. As a consequence, more research is now being directed to develop and test new materials that can improve DF performance in harsh conditions. Recent developments and applications associated with polymeric treatment agents (synthetic polymers; SP) as DF additives have met with some success in HPHT and excessively saline conditions with respect to preventing lost circulation. However, certain challenges remain regarding the quality and sustainability of SP in harsh environments. This review describes the evolution of DF compositions and additives, with particular emphasis on the types of SP deployed, and their advantages and disadvantages, considering the major obstacles they must combat in the harsh subsurface conditions that are typically encountered, which mainly are HPHT, salt contamination, and lost circulation. This review reveals the key monomers that can be used to generate SPs, combat salt contamination and be deployed in HPHT conditions, as well as identifying their advantages and disadvantages. Combating lost circulation risks and the remedial actions and the SP materials required to do so in harsh conditions is addressed in detail. The most important monomers used in the polymerization SP that are deployed in harsh conditions are identified and their benefits and drawbacks are contrasted. Moreover, new advances in SP and their modification to address DF problems are discussed. Environmental aspects of the toxicological value and class of utilized monomers in the synthesis of SPs are evaluated. The economic aspects of SP deployments in DF and the cost of the most utilized monomers in the synthesis of SPs are assessed. The information reviewed leads to recommendations for future research addressing the prevailing knowledge and SP property gaps. [ABSTRACT FROM AUTHOR]

• Nanotechnology offers many potential solutions to resolve drill-fluid issues. • Many NPs have been tested including metals, carbon, ceramics and polymers. • Silica and alumina nanoparticles (NP) are most beneficial for drill-fluid rheology. • Certain NP are beneficially effective at high temperatures and pressures. • NP manufacturing costs are the main hurdle limiting their wider use in drilling. Nanotechnology remains a relatively new innovation for the oil and gas industry. However, many applications have now become an integral part of some of its key technological processes. Nanotechnology is being widely applied, especially to improve the performance of drilling equipment, wellbore fluids, and improved/enhanced oil and gas recovery methods. Some drilling equipment, such as drilling bits and wellbore tubulars, are being fabricated using nanomaterials. Here, we review the evolution of relevant nanotechnology applications, manufacturing methods of suitable nanoparticles, their classification, and the various applications of nanotechnology in drilling engineering, especially related to drilling fluids. Applications involving water- and oil-based drilling fluids are reviewed. It is apparent that the nanoparticles used to date have tangibly improved several drilling processes. Silica and alumina nanoparticles have proved to be highly effective at improving the rheology, filtration, and thermal stability of drilling fluids. Environmental impacts and costs of materials/fluids are of paramount concern when selecting materials for deployment as drilling fluids, and these factors are addressed in this review. By providing a comprehensive understanding of existing, successful nanotechnology applications in drilling fluids, this review provides a guide to outstanding research issues to be solved and potential future applications in this field. [ABSTRACT FROM AUTHOR]

• Drilling fluids containing associative synthetic polymer, bentonite, and limestone are studied. • A mesoscopic model is developed describing the drilling fluids' rheological behavior. • The model predicts the material functions under steady-state and transient conditions. • The model evaluates the contribution of each component in the drilling fluid rheology. Employing an effective rheological model for the flow of drilling fluid that can accurately predict changing conditions is of significant importance in drilling fluid optimization. Traditional generalized Newtonian models cannot predict the time change condition, viscoelastic behavior, role of each component, or microstructural behaviors within the fluid. Consequently, the present research aims to develop constitutive equations in the framework of generalized bracket formalisms and the extra tensor concept that connect the microscopic and macroscopic properties and can overcome the aforementioned problems of traditional rheological models. The developed model is applicable for drilling fluid as a suspension system containing associative synthetic polymer, bentonite, and limestone suspended in water; where simple structures of flexible dumbbells, disks, and hard spheres, respectively, are representative of each component in the system. Five samples of drilling fluid with different additive concentrations were prepared and rheological testing was performed on these samples. To obtain the fixed parameters of the model, scanning electron microscope and particle size analyses were conducted on the dried powder and dispersed particles of bentonite and limestone to characterize the shape and size of the particles. The adjustable parameters of the model were then obtained by fitting it using the gathered experimental rheological data. The outcomes of the study revealed that the novel developed model can accurately predict rheological material functions, including shear viscosity and the first normal stress coefficient under transient and steady-state conditions. Furthermore, the presented model is capable of distinguishing the contribution of each component in the drilling fluid rheology. [ABSTRACT FROM AUTHOR]

In this study, effects of dried saffron purple petals (SPP) powder were examined on the rheological, fluid loss, and corrosion inhibition properties of bentonite-based drilling fluids. Drilling fluids containing different amounts of the SPP powder were prepared and their rheological behavior was investigated via the rotary viscometry and rheometric mechanical spectroscopy (RMS). Rotary viscometer results were fitted with Power-law, Bingham plastic, and Herschel-Bulkley models and the obtained data were compared with that of the base mud. All models fitted the rotary viscometer data with the determination coefficients higher than 0.93. The presence of 3 wt% of the SSP in the fluid significantly improved the fluid's yield stress and consistency index by 223.3% and 94.4%, respectively compared to those of the base mud. The filtration performance and cake formation results confirmed a 45% enhancement in the permeation barrier behavior of the fluid containing 3 wt% SPP powder. Electrochemical impedance measurements illustrated that the total impedance of the mild steel increase from 299 to 39,800 Ω cm2 in the presence of 3 wt% SPP powder compared to the control sample. Additionally, potentiodynamic polarization plots indicated a mixed-type inhibition mechanism for the SPP powder with the corrosion rate of 0.004 mm year−1 and inhibition efficiency of 96.5% in the presence of 3 wt% SPP powder. The morphology of mild steel plates immersed in SPP filled fluids demonstrated the formation of corrosion inhibitive layers on the metallic surface, which hindered the anodic and cathodic reactions while a considerable amount of rusts and corrosion products formed for the plate immersed in the base mud. Furthermore, having SPP as an organic green additive would leave no damaging effects on the surrounding environment and ecosystem. Unlabelled Image • Drilling fluids containing different contents of saffron petal powder were prepared. • Rheological properties of drilling fluids were studied at various conditions. • Filtration behavior of drilling fluids containing saffron petal powder was studied. • Corrosion protection performance of drilling fluids was investigated by EIS. • Rheological, filtration and corrosion protection properties enhanced significantly. [ABSTRACT FROM AUTHOR]