Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: a comprehensive review

Fluid loss to subsurface formations is a challenging aspect during drilling operations in petroleum industry. Several other drilling issues such as fluid influx and pipe sticking can be triggered in such scenarios, posturing a significant risk to rig personnel, environment, and economical drilling. Therefore, prediction and early detection of lost circulation events are required for safe and economic drilling operation. Several theoretical studies have been performed to detect and predict fluid loss event during hydrocarbon extraction. This paper reviews the existing conventional and intelligent models developed for early detection and prediction of lost circulation events. These predictive and detecting models comprise of Artificial Intelligence (AI) algorithms that require improvements for data reduction, universal prediction and compatibility. The review also covers several sensor-based techniques, different geostatistical-based models and Pressure-While-Drilling (PWD) tools for their applications in early loss circulation detection. In addition, loss circulation zones types, severity level, scenario and common preventive measures are also included in this review. This study aims to provide a systematic review of the published literature from the last forty years on the developed conventional and intelligent models for detection and prediction of fluid loss events and emphasizes on increasing AI involvement for precise results

Application of machine learning to determine the shear stress and fltration loss properties of nano‑based drilling fuid

A detailed understanding of the drilling fuid rheology and fltration properties is essential to assuring reduced fuid loss during the transport process. As per literature review, silica nanoparticle is an exceptional additive to enhance drilling fuid rheology and fltration properties enhancement. However, a correlation based on nano-SiO2-water-based drilling fuid that can quantify the rheology and fltration properties of nanofuids is not available. Thus, two data-driven machine learning approaches are proposed for prediction, i.e. artifcial-neural-network and least-square-support-vector-machine (LSSVM). Parameters involved for the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate fltration volume. A feed-forward multilayer perceptron is constructed and optimised using the Levenberg–Marquardt learning algorithm. The parameters for the LSSVM are optimised using Couple Simulated Annealing. The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R2 (coefcient of determination) value higher than 0.99 and MAE (mean absolute error) and MAPE (mean absolute percentage error) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data

Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids

The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s−1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids

A review on recent advances of cellulose acetate membranes for gas separation

This review thoroughly investigates the wide-ranging applications of cellulose-based materials, with a particular focus on their utility in gas separation processes. By focusing on cellulose acetate (CA), the review underscores its cost-effectiveness, robust mechanical attributes, and noteworthy CO2 solubility, positioning it as a frontrunner among polymeric gas separation membranes. The synthesis techniques for CA membranes are meticulously examined, and the discourse extends to polymeric blend membranes, underscoring their distinct advantages in gas separation applications. The exploration of advancements in CA-based mixed matrix membranes, particularly the incorporation of nanomaterials, sheds light on the significant versatility and potential improvements offered by composite materials. Fabrication techniques demonstrate exceptional gas separation performance, with selectivity values reaching up to 70.9 for CO2/CH4 and 84.1 for CO2/N2. CA/PEG (polyethylene glycol) and CA/MOF (metal–organic frameworks) demonstrated exceptional selectivity in composite membranes with favorable permeability, surpassing other composite CA membranes. Their selectivity with good permeability lies well above all the synthesised cellulose. As challenges in experimental scale separation emerge, the review seamlessly transitions to molecular simulations, emphasizing their crucial role in understanding molecular interactions and overcoming scalability issues. The significance of the review lies in addressing environmental concerns, optimizing membrane compositions, understanding molecular interactions, and bridging knowledge gaps, offering guidance for the sustainable evolution of CA-based materials in gas separation technologies

Settling characteristics of nanoparticles in ethanol-water mixtures

Nanofluids are dilute suspensions of functionalized nanoparticles having diameter less than 100 nm. Settling of nanoparticles due to gravity is a natural phenomenon and the only predicament towards their usage in industrial scale. Knowledge of sedimentation behavior of nanoparticles is a topic of great interest as not much information is available on the settling characteristics of nanosuspensions in different combinations. Formation of agglomerates due to aggregation among particles in a nanosuspension promotes the sedimentation process. Agglomeration takes place when randomly moving particles come closer in a base liquid and join together due to strong attractive forces to form particle clusters

Investigation on the stability, thermal characteristics and natural convection heat transfer in oil-based nanofluids.

Thermal oils are widely used as cooling media in heat transfer processes. However, their potential has not been utilised exquisitely due to low thermal properties. The addition of nanoparticles in thermal oil can improve thermal properties

Heat Transfer Performance of Different Fluids During Natural Convection in Enclosures with Varying Aspect Ratios

The heat transfer process takes place in numerous applications through the natural convection of fluids. Investigations of the natural convection heat transfer in enclosures have gained vital importance in the last decade for the improvement in thermal performance and design of the heating/cooling systems. Aspect ratios (AR=height/length) of the enclosures are one of the crucial factors during the natural convection heat transfer process. The investigated fluids consisting of air, water, engine oil, mercury, and glycerine have numerous engineering applications. Heat transfer and fluid flow characteristics are studied in 3-dimensional rectangular enclosures with varying aspect ratios (0.125 to 150) using computational fluid dynamics (CFD) simulations. Studies are carried out using the five different fluids having Prandtl number range 0.01 to 4500 in rectangular enclosures with the hot and cold surface with varying temperature difference 20K to 100K. The Nusselt number and heat transfer coefficients are estimated at all conditions to understand the dependency of ARs on the heat transfer performance of selected fluids. Temperature and velocity profiles are compared to study the flow pattern of different fluids during natural convection. The Nusselt number correlations are developed in terms of aspect ratio and Rayleigh number to signify the natural convection heat transfer performance

A comparative study on meeting the energy demand from biogas in Pakistan

Pakistan is facing energy crises from past decade due to experiencing increase in energy demands. This current work is a comparative study to meet the energy demand from production of biogas from waste solid materials. The main emphasis is to treat biomass, manure, municipal waste, sewage and green waste to produce biogas using different technologies. At the ends there are some suggestions for effective planning of sustainable energy exploitation and facilitate for technology solution of further research. These suggestions are very useful for meeting the energy demand in Pakistan as well as for third world countries.Upprättat; 2011; 20120904 (abrfai

Reservoir Performance Prediction in Steam Huff and Puff Injection Using Proxy Modelling

Steam huff and puff injection is one of the thermal EOR methods in which steam is injected in a cyclical manner alternating with oil production. The cost and time inefficiency problem of reservoir simulation persists in the design of a steam huff and puff injection scheme. Building predictive proxy models is a suitable solution to deal with this issue. In this study, predictive models of the steam huff and puff injection method were developed using two machine learning algorithms, comprising conventional polynomial regression and an artificial neural network algorithm. Based on a one-well cylindrical synthetic reservoir model, 6043 experiment cases with 28 input parameter values were generated and simulated. Outputs from the results such as cumulative oil production, maximum oil production rate and oil rate at cycle end were extracted from each simulation case to build the predictive model. Reservoir properties that could change after an injection cycle were also modeled. The developed models were evaluated based on the fitting performance from the R-square value, the mean absolute error (MAE) value and the root mean square error (RMSE) value. Then, Sobol analysis was conducted to determine the significance of each parameter in the model. The results show that neural network models have better performance compared to the polynomial regression models. Neural network models have an average R-square value of over 0.9 and lower MAE and RMSE values than the polynomial regression model. The result of applying the Sobol analysis also indicates that initial reservoir water saturation and oil viscosity are the most important parameters for predicting reservoir production performance

Rheological profile of graphene-based nanofluids in thermal oil with hybrid additives of carbon nanotubes and nanofibers

International audienceThe evolution in nanofluid technology in the last few decades has proved the prodigious potential in several applications, especially thermal management and lubrication. An extensive investigation of nanofluid's rheological profile is vital to characterize the fluid flow behavior. This study signifies the rheological aspects of graphene and its hybrid nano-dispersions in thermal oil. The experimental investigation involves three sets of nanofluids containing graphene, graphene-carbon nanotubes, and graphene-carbon nanofiber hybrid nanofluid dispersions in thermal oil with varying loadings (0–2 mass%). The flow behaviors of all sets of nanofluids are measured at a wide shear range of 1–2000 s−1 and five different temperatures from 298 K to 338 K. The morphology and stability are validated by performing several characterizations for nanomaterials and nanofluids. Non-Newtonian fluid behavior is observed in all nanofluids. This study reveals a few interesting outcomes where the fluid behaving as a Power Law model is shifted to the Herschel-Bulkley model at high loadings of nanomaterials. A comparative analysis illustrates that both hybrid additives act as viscosity reducers for graphene-based nanofluids, where graphene-carbon nanofibers hybrid nanofluids exhibit noticeable reduction. A parametric analysis is performed on the viscous behavior involving the impact of shear rate, temperature, nanomaterial loading, and surfactant concentration. The increment in viscosity shoots up to 180 % for graphene-nanofluid at the 2000 s−1 shear rate and 338 K temperature, but still exhibits shear thinning phenomena. A correlation is also proposed for the nanofluid viscosity in terms of nanomaterial loading and temperature, indicating a good agreement at varying shear rates