Evaluation of using micronized saudi calcite in ilmenite-weighted water-based drilling fluid.

A high-density water-based drilling fluid (WBDF) is crucial for maintaining wellbore stability, controlling formation pressures, and optimizing drilling performance in challenging subsurface conditions. In the present research, the effect of micronized calcium carbonate (calcite), extracted from the Aruma formation outcrop, is evaluated as one of the additives that could be added to the ilmenite-weighted WBDF to enhance and optimize its properties. Various concentrations of Calcite microparticles were introduced into identical fluid formulations to assess their impact. The concentrations ranged from 0, 10, 20, to 30 lb/bbl, providing a comprehensive examination of the effects of calcite microparticles across a spectrum of concentrations within the fluid. The results highlighted that adding Barite microparticles to the WBDF revealed a notable enhancement in rheological properties. Specifically, the yield point demonstrated an increase of 37%, 37%, and 11% for concentrations of 10, 20, and 30 lb/bbl of calcite, respectively. Equally significant, high-pressure-high-temperature (HPHT) filtration analysis indicated a considerable enhancement for the fluids containing calcite microparticles. A reduction of 14.5%, 24.6%, and 13% were observed in HPHT filtrate for concentrations of 10 lb/bbl, 20 lb/bbl, and 30 lb/bbl respectively. Simultaneously, there is a reduction in filter cake thickness by 20%, 40%, and 20%, respectively. No ilmenite settling was observed in the sample containing 20 lb/bbl of calcite, unlike the other concentrations. These diverse results strongly suggest that the optimal concentration for calcite microparticles is 20 lb/bbl. The combined utilization of the optimal concentration of calcite microparticles alongside the established additives proves to be an effective strategy for optimizing the ilmenite-weighted WBDF performance in terms of both thermal stability and rheological behavior.

Optimizing hematite filter cake treatment using reducing agents.

In drilling operations, the formation of a filter cake is crucial for well stability, but its removal post-drilling is essential to restore rock formation productivity. This study focuses on hematite-based filter cakes and investigates factors influencing their solubility and removal, addressing a significant knowledge gap in the field. The research methodology involves examining the effects of various factors, including types and concentrations of reducing agents, temperature, particle size, and treatment duration, on the dissolution process. Notably, Nuclear Magnetic Resonance (NMR) tests are employed to assess the treatment's impact on core porosity. Among the diverse reducing agents examined, ferrous chloride emerges as the optimal choice for effectively enhancing hematite solubility. Particularly, a composite solution of ferrous chloride (10 wt.%) and hydrochloric acid (6 wt.%), was highly efficient demonstrated by exhibiting rapid solubilization of hematite filter cakes. A removal efficiency of approximately 99%, with a parallel enhancement in core permeability was achieved. NMR tests reveal the treatment's success in reinstating the porosity system, which had undergone reduction due to drilling fluid particles. Crucially, the solution exhibits a considerably lower corrosion rate than concentrated hydrochloric acid, highlighting its potential to mitigate environmental concerns while ensuring efficient filter cake removal. The findings of this research provide valuable insights into optimizing post-drilling operations, balancing environmental sustainability and operational efficiency. The identified composite solution offers a promising approach to efficient filter cake removal while mitigating environmental concerns associated with corrosion. Overall, this study contributes to advancing the understanding and practice of well productivity enhancement in the oil and gas industry.

Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters.

The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (k) obtained through core analysis. Three machine learning models, namely, decision trees (DTs), random forest (RFs), and support vector machines (SVMs), were employed and evaluated for their effectiveness in predicting porosity and permeability. The results demonstrate promising performance across the different data sets. All three models achieved correlation coefficients (R) higher than 0.91 in predicting porosity. The RF model exhibited accurate predictions of permeability, achieving R values surpassing 0.92 in the various data sets. While the DT model displayed slightly lower performance, with the R-value decreasing to 0.88 in the testing data set, the SVM model suffered from overfitting, with R values dropping to 0.83 in the testing data set. The novelty of this work lies in the successful application of machine learning models to the real-time prediction of reservoir properties, providing a practical and efficient solution for the oil and gas industry. By achieving correlation coefficients exceeding 0.91 and showcasing the models' efficacy in a dynamic testing data set, this study paves the way for improved decision-making processes and enhanced exploration and production activities. The innovative aspect lies in the utilization of drilling parameters for timely and cost-effective estimation, transforming conventional reservoir evaluation methods.

Perlite incorporation for sedimentation reduction and improved properties of high-density geopolymer cement for oil well cementing.

Portland cement (PC) is known for its environmental and technical concerns and massive energy consumption during manufacturing. Geopolymer cement is a promising technology to totally replace the use of PC in the oil and gas industry. Although geopolymers are widely used in the construction industry, it is yet to see a full-scale application in the petroleum industry. High-density geopolymer cement development is essential to substitute heavy-weight Portland cement slurries for high pressure well cementing applications. Sedimentation issue is associated with high-density cement slurries which use high specific gravity solids such as weighting materials. This problem causes heterogeneity and density variation along the cemented sections. The main target of this work is to evaluate the use of perlite powder to address the sedimentation issue in the heavy weight geopolymer systems. Hematite-based Class F fly ash (FFA) geopolymer cement slurries with perlite concentrations of 0, 1.5, and 3% by weight of binder (BWOB) were prepared. The sedimentation problem was investigated using three techniques: API method, nuclear magnetic resonance (NMR), and computed tomography (CT) scan. The perlite effects on different geopolymer properties such as unconfined compressive strength (UCS), porosity, elastic and rheological properties were assessed. The results proved that perlite incorporation in high-density hematite-based FFA geopolymer significantly reduced sedimentation issue by increasing yield point and gel strength. NMR and CT scan showed that perlite decreases porosity and density variation across the geopolymer samples. The UCS increased with increasing perlite percentage from 0 to 3%. The measured Young's moduli (YM) and Poisson's ratios (PR) showed that the developed perlite based geopolymer systems are considered more flexible than Class G cement systems. It was found that the optimum perlite concentration is 3% BWOB for tackling sedimentation and developing a slurry with acceptable mixability and rheological properties.

Evaluating the Effect of Claytone-EM on the Performance of Oil-Based Drilling Fluids.

This study provides a detailed characterization and evaluation of Claytone-EM as a rheological additive to enhance the performance of oil-based drilling fluids (OBDFs) under high-pressure, high-temperature (HPHT) conditions. It also offers a comparative evaluation of the effectiveness of Claytone-EM with an existing organoclay, analyzing their mineral and chemical compositions, morphologies, and particle sizes. A series of experiments are performed to evaluate Claytone-EM's influence on crucial drilling mud properties, such as mud density, electrical stability, sagging tendency, rheology, viscoelastic properties, and filtration properties, to formulate a stable and high-performing OBDF. Results indicated that Claytone-EM had no significant impact on mud density but remarkably enhanced emulsion stability. Claytone-EM effectively mitigated sagging issues under both static and dynamic conditions, leading to improvements in the plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and YP/PV ratio. The PV, YP, AV, and YP/PV ratios were improved by 11, 85, 28, and 66% increments, respectively, compared with those of the drilling fluid formulated with MC-TONE. The addition of Claytone-EM resulted in enhancing gel strength and improving the filtration properties of the drilling fluid. The filtration volume was reduced by 2% from 5.0 to 4.9 cm3, and the filter cake thickness had a 13% reduction from 2.60 to 2.26 mm. These findings highlight Claytone-EM as a valuable additive for enhancing OBDF performance, particularly under challenging HPHT conditions. Its ability to provide emulsion stability, reduce static and dynamic sag, and control filtration holds the potential to enhance drilling operations, minimize downtime, and bolster wellbore stability. This study acknowledges certain limitations, including its temperature range, which could benefit from exploration at extreme temperatures. Additionally, the absence of flow experiments limits a comprehensive understanding of sag effects, and further research and field-scale evaluations are recommended to validate and optimize the application of Claytone-EM in OBDFs.

Investigating the efficacy of novel organoclay as a rheological additive for enhancing the performance of oil-based drilling fluids.

Oil-based drilling fluids (OBDFs) are extensively used in the drilling industry due to their superior performance in challenging drilling conditions. These fluids control wellbore stability, lubricate the drill bit, and transport drill cuttings to the surface. One important component of oil-based drilling fluids is the viscosifier, which provides rheological properties to enhance drilling operations. This study evaluates the effectiveness of Claytone-IMG 400, a novel rheological agent, in enhancing the performance of OBDFs under high-pressure and high-temperature (HPHT) conditions. A comparative analysis was conducted with a pre-existing organoclay (OC) to assess the improvements achieved by Claytone-IMG 400. The OCs were analyzed using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), and particle size distribution (PSD) to identify their mineral and chemical compositions, morphologies, and particle sizes. The drilling fluid density, electrical stability, sagging tendency, rheological properties, viscoelastic properties, and filtration properties were studied to formulate a stable and high-performance drilling fluid. The results confirmed that the novel OC does not affect the drilling fluid density but enhances the emulsion stability with a 9% increment compared with the drilling fluid formulated with MC-TONE. The sagging experiments showed that Claytone-IMG 400 prevented the sagging issues in both static and dynamic conditions. Also, Claytone-IMG 400 improved the plastic viscosity (PV), yield point (YP), and apparent viscosity (AV). The PV, YP, and AV were improved by 30%, 38%, and 33% increments respectively compared with the drilling fluid formulated with MC-TONE. The YP/PV ratio increased with a 6% increment from 1.12 to 1.19. Moreover, the gel strength (GS) was significantly increased, and the filtration properties were enhanced. The filtration volume was reduced by 10% from 5.0 to 4.5 cm3, and the filter cake thickness had a 37.5% reduction from 2.60 to 1.89 mm. The novelty of this study is highlighted by the introduction and evaluation of Claytone-IMG 400 as a new rheological additive for safe, efficient, and cost-effective drilling operations. The results indicate that Claytone-IMG 400 significantly improves the stability and performance of OBDFs, thereby reducing wellbore instability and drilling-related problems.

Properties and Performance of Oil Well Slurry and Cement Sheath Incorporating Nano Silica: A Review.

The oil well cementing job is the operation in which a cement paste is pumped to fill the annulus behind the casing. Inclusion of nanomaterials in oil well cement results in improving the cement properties. This paper provides a comprehensive overview of incorporating nanosilica into oil well cement, addressing various aspects of the nanosilica manufacturing process, dispersion challenges, the impact on cement hydration and properties, as well as the operational challenges. The addition of nanosilica is found to enhance cement properties such as hydration rate, compressive strength at low temperatures, and resistance to deterioration at high temperatures. However, challenges arise, including increased viscosity and the need for higher water content. Dispersion of nanosilica into cement slurry remains a difficulty, compounded by the high manufacturing cost, limiting its practical application. The paper recommends further research to improve nanosilica dispersion, explore cost-effective raw materials, and overcome operational challenges for broader utilization in oil well cementing.

Foamed Cement Applications in Oil Industry Based on Field Experience: A Comprehensive Review.

Foam cement is a versatile cementing material that has found numerous applications in the oil and gas industry. As research continues to advance and improve the properties of foam cement, it is likely that we will see an increased use of this material in the years to come. This review aims to summarize the current state of the art and the latest developments in the utilization of foam cement in oil fields. The study focuses on the key benefits of foam cement, including its light weight, excellent flow properties, ability to maintain its structural integrity over time, and high compressive strength. It also examines its various applications in oil field operations, such as cementing against fragile formations, well abandonment, zonal isolation, cementing offshore wells, and well remedial cementing. Furthermore, the paper evaluates the various factors that influence the performance of foam cement, such as the mixing design, foam structure, and stability. In addition, the methods for evaluating the foamed cementing job and the integrity of the formed cement sheath are also presented. The review also highlights the current challenges and limitations of foam cement technology that should be considered when using foamed cement in oil field applications and discusses the future directions for its development and optimization. This review provides a comprehensive overview of the applications of foam cement in oil fields and will be of great interest to engineers, researchers, and practitioners in the oil and gas industry.

Impact of Eco-Friendly Drilling Additives on Foaming Properties for Sustainable Underbalanced Foam Drilling Applications.

Underbalanced foam drilling stands out as a drilling technique acclaimed for its capacity to enhance safety and efficiency in operations. Utilizing foams as drilling fluids offers several benefits over traditional methods, including lower density, diminished formation damage, and augmented borehole stability. However, the persistent challenge of sustaining foam stability in demanding conditions, particularly amid elevated water salinity and alkaline environments, remains a critical issue. Current literature lacks comprehensive insights into foam stability under such specific circumstances, raising concerns about the practicality of numerous reported foaming agents in field applications. This study aims to fill this knowledge void to align with industry standards. With a heightened focus on sustainability due to mounting environmental considerations, the research explores the use of an eco-friendly surfactant, ammonium alcohol ether sulfate (AAES). Additionally, the investigation delves into the impact of environmentally friendly drilling additives-polyanionic cellulose (PAC), carboxymethyl cellulose (CMC), and starch-on the stability of bulk foam under mildly alkaline conditions. Employing a dynamic foam analyzer, diverse foam properties of AAES foams were assessed, encompassing stability, foamability, and bubble structure. The results demonstrated that the optimal concentrations of the tested additives, in the order of PAC > CMC > starch, significantly prolonged the half-life of the AAES foam bubbles. The introduction of PAC and CMC additives elevated the viscosity of AAES foaming solutions, enhancing the liquid retention within the foam structure. In contrast, starch addition exerted no influence on the solution viscosity and did not impede liquid drainage, although it did reduce bubble coalescence. Furthermore, the PAC- and CMC-based AAES foams manifested as considerably wetter foams with a rounded bubble structure, while the starch-based AAES foam exhibited a dry foam characterized by a distinct polyhedral bubble structure. These findings offer valuable insights into the potential application of the AAES surfactant in foam drilling, showcasing its efficacy in improving foam stability and contributing to the evolution of eco-friendly drilling practices.

Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations.

Foam, a versatile underbalanced drilling fluid, shows potential for improving the drilling efficiency and reducing formation damage. However, the existing literature lacks insight into foam behavior under high-pH drilling conditions. This study introduces a novel approach using synthesized seawater, replacing the conventional use of freshwater on-site for the foaming system's liquid base. This approach is in line with sustainability objectives and offers novel perspectives on foam stability under high-pH conditions. Experiments, conducted with a high-pressure, high-temperature (HPHT) foam analyzer, investigate how pressure and temperature affect foam properties. The biodegradable foaming agent ammonium alcohol ether sulfate (AAES) is employed. Results demonstrate that the pressure significantly impacts foam stability. Increasing pressure enhances stability, reducing decay rates and promoting uniform bubble sizes, especially at lower temperatures. This highlights foam's capacity to withstand high-pressure conditions. Conversely, the temperature plays a substantial role in foam decay, particularly at elevated temperatures (75 and 90 °C). Decreased liquid viscosity accelerates the liquid drainage and foam decay. While pressure mainly influences the AAES foam stability at temperatures up to 50 °C, temperature becomes the dominant factor at higher temperatures. Temperature's impact on foamability is minimal under constant pressure, maintaining consistent gas volume for maximum foam height. However, foam stability is sensitive to temperature variations, with increasing temperature leading to a more significant bubble size increase gradient. These findings stress the importance of considering temperature effects in foam drilling, particularly in deep and high-temperature environments. AAES foam exhibits stability at lower temperatures, making it suitable for surface and intermediate drilling. Understanding temperature-induced changes in foam structure and bubble size is essential for optimizing performance in high-temperature and deep drilling scenarios.

Experimental Investigation of Using Manganese Monoxide as a Hydrogen Sulfide Scavenger for Aqueous Drilling Fluids.

One of the most serious safety and health concerns during drilling oil and gas wells is the potential release of hydrogen sulfide (H2S) to the surface, exposing workers to high risks. Serious corrosion-related damage to handling equipment is also inevitable in the presence of H2S. Various H2S scavengers have been utilized, but each has its pros and cons; hence, research is continuing to develop an optimum and feasible scavenger. Since manganese monoxide (MnO) is a reactive metal oxide with high oxidation and absorption capabilities, it may have the potential to effectively scavenge H2S during drilling operations when included in drilling mud formulations. Consequently, the key aim of this work is to investigate the H2S scavenging performance of the aqueous drilling fluid containing MnO. This work studied the impact of MnO addition on the drilling mud's alkalinity, rheological behavior, filtration performance, and corrosion tendency. The experiments were also conducted for mud without a scavenger and a fluid containing the SourScav commercial scavenger, which serves as a benchmarking reference. The findings demonstrated that MnO performed exceptionally well for H2S scavenging where it boosted the aqueous mud's scavenging capacity from 84.3 to 426.2 mg of H2S/L of mud, showing more than 400% improvement relative to the base mud. Additionally, this scavenging performance is about 2.1 times higher than that of the commercial scavenger. As opposed to SourScav, MnO maintained the mud's pH at a safe level above 10. The addition of either MnO or SourScav did not weaken the mud rheology and provided practically satisfactory rheological parameters. Both SourScav and MnO marginally increased the formed filter-cake thickness from 2.9 to 3.9 mm with a slight increment in the filtrated volume but still within the acceptable limits. The corrosion test indicated the noncorrosive characteristics (i.e., the corrosion rate was nearly zero) of MnO and the commercial scavenger. This study illustrates the promising utilization of MnO as a cost-effective H2S scavenger, enhancing the efficiency and safety of drilling operations.

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling.

Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods. Data from two wells (A and B) in the Middle East, encompassing drilling mechanical parameters such as rate of penetration (ROP), drill string rotation (DSR), pumping rate (Q), standpipe pressure (SPP), weight on bit (WOB), and torque, were collected for real-field analysis. Machine learning models including Gaussian naive Bayes (GNB), logistic regression (LR), and linear discriminant analysis (LDA) were trained and tested on the data set from well A, while the data set from well B was utilized for model validation as unseen data. The formations of wells A and B consist of four lithologies, namely, sandstone, anhydrite, carbonate/shale, and carbonates, necessitating the development of multiclass classification models. The drilling parameters, specifically the WOB and ROP, exhibited a strong influence on lithology identification. Among the models, GNB demonstrated exceptional performance in predicting formation lithology from the drilling parameters, achieving accuracy and nearly perfect precision, recall, and F1 score for the different classes. LDA and LR models accurately predicted sandstone and carbonate lithologies, although some misclassifications occurred in approximately 5% of points for anhydrite and around 20% in carbonate/shale formations. During validation, the models demonstrated accuracies of around 0.96, 0.95, and 0.92 for the GNB, LR, and LDA, respectively. The study highlights the efficacy of the developed machine learning models in accurately predicting the formation lithology and tops in real time. This is achieved by utilizing readily available drilling parameters, making the approach highly accurate and cost effective by leveraging existing real-time drilling data.

Evaluation of Using Fly Ash as a Weighing Material for Oil-Based Drilling Fluid.

Innovation and sustainability are essential in the fast-changing oil and gas business. Fly ash, a byproduct of coal combustion in power plants and factories, has become a valuable resource in many industries, changing the concept of waste materials. Fly ash is essential to sustainable development and environmental care due to its unique qualities and multiple applications. In the drilling industry, a well-designed drilling fluid is essential and this requires the use of various additives that serve specific functions to achieve a successful borehole. This study investigates the use of fly ash as a weighing material in oil-based mud, with the intent to develop an economically acceptable drilling fluid system using industrial waste. The study compared fly ash to three commonly used weighing materials in the drilling industry: calcium carbonate (CaCO3), barite (BaSO4), and ilmenite (FeTiO3). Drilling fluids were prepared using these weighing materials at various weights, and their properties (density, electrical stability, rheological features, and filtration properties) were measured using API-recommended methods. The rheology and filtration tests were conducted at elevated temperatures (350 °F). The results indicate that fly ash has the potential to be a useful weighing material in drilling operations. It can increase the fluid density up to 10 ppg without affecting the rheological properties at 350 °F. Additionally, the electrical stability of the drilling fluid was enhanced compared to the other used weighing materials. The addition of fly ash also improved rheological characteristics such as plastic viscosity, yield point, and gel strength without affecting HPHT filtration properties. The carrying capacity was improved by 53 and 86% over calcium carbonate and barite, respectively. Overall, the findings suggest that fly ash can be a viable alternative to other weighing materials in the recommended density range.

Micronized calcium carbonate to enhance water-based drilling fluid properties.

Advanced drilling technique requires competent drilling fluids. This study tests micronized calcium carbonate (CaCO3) as a water-based drilling fluid (WBDF) additive. CaCO3 microparticles were extracted from Aruma formation outcrop and studied for structural, colloidal stability, morphology, and particle size distribution. WBDF systems were prepared with varying quantities of CaCO3 microparticles, including 0, 15, 30, and 45 lb/bbl, respectively. The addition of CaCO3 microparticles was investigated in terms of the rheological, high pressure-high temperature (HPHT) filtration, barite sagging, density, and pH. The results showed that CaCO3 microparticles are stable at a pH greater than 8. Moreover, fluid containing CaCO3 microparticles exhibited an enhancement in rheological properties. The yield point increased by 29%, 34%, and 37% for 15, 30, and 45 lb/bbl of CaCO3 respectively. In addition, the HPHT filtration also showed that CaCO3 has a significant improvement in both filtration loss and filter cake thickness. The filter cake thickness decreased by 17%, 40%, and 65% at 15, 30, and 45 lb/bbl of CaCO3 respectively. Static and dynamic sag maintained in a safe range at 30 lb/bbl of CaCO3 microparticles. This study showed that using CaCO3 microparticles along with conventional fluid additives improved the thermal stability and rheological properties of drilling fluid.

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section.

The downhole vibration is one of the most crucial factors that affect downhole equipment performance and failure, besides wellbore instability. Downhole tool failure, hole problems, mechanical energy loss, and ineffective drilling performance are commonly associated with drillstring high vibration levels. The high vibration level will lead to more complications while drilling that might cause nonproductive time and extra cost. Meanwhile, the downhole sensors for detecting the drillstring vibrations add more cost to the operation. Consequently, the new solutions based on technology capabilities provide a powerful tool to integrate and interpret the drilling data for the best use of @@the data for operation performance enhancement. This study provides a successful application for utilizing the surface drilling data to automate drillstring vibration detection during the drilling curve section employing machine learning (ML) techniques. The axial, torsional, and lateral vibration modes are detected through testing four ML techniques named the @@adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), functional networks (FN), and support vector machines (SVMs) with real field data. The models' development was achieved by comprehensive study starting from data gathering, wrangling, statistical analysis, developing the ML models, evaluating the model prediction accuracy, and reporting the high accuracy results. The developed models were evaluated, and results showed that ANFIS and SVM models provided the highest accuracy with a coefficient of correlation (R) ranging from 0.9 to 0.99 followed by the RBF and FN models through model training and testing (R ranging from 0.82 to 0.96). Validating the models over unseen data confirmed the high accuracy prediction for the three vibration modes. Generally, the developed models provided technically accepted accuracy with R higher than 0.93 and AAPE less than 2.8% for SVM and ANFIS models while FN and RBF showed R between 0.82 and 0.95 and AAPE less than 5.7% between actual readings and predictions. Based on these results, the developed ML algorithm might be utilized as an intelligent solution to autodetect downhole vibration while drilling from surface sensor data only, which will save the downhole tool cost.

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods.

Drilling rate of penetration (ROP) is one of the most important factors that have their significant effect on the drilling operation economically and efficiently. Motorized bottom hole assembly (BHA) has different applications that are not limited to achieve the required directional work but also it could be used for drilling optimization to enhance the ROP and mitigate the downhole vibration. Previous work has been done to predict ROP for rotary BHA and for rotary steerable system BHA; however, limited studies considered to predict the ROP for motorized BHA. In the present study, two artificial intelligence techniques were applied including artificial neural network and adaptive neurofuzzy inference system for ROP prediction for motorized assembly in the rotary mode based on surface drilling parameters, motor downhole output parameters besides mud parameters. This new robust model was trained and tested to accurately predict the ROP with more than 5800 data set with a 70/30 data ratio for training and testing respectively. The accuracy of developed models was evaluated in terms of average absolute percentage error, root mean square error, and correlation coefficient (R). The obtained results confirmed that both models were capable of predicting the motorized BHA ROP on Real-time. Based on the proposed model, the drilling parameters could be optimized to achieve maximum motorized BHA ROP. Achieving maximum ROP will help to reduce the overall drilling cost and as well minimize the open hole exposure time. The proposed model could be considered as a robust tool for evaluating the motorized BHA performance against the different BHA driving mechanisms which have their well-established models.

The Effect of Olive Waste on the Rheological Properties, Thickening Time, Permeability, and Strength of Oil Well Cement.

In the oil and gas industry, cementing is a very important process to maintain the stability of the well. The cement can provide an effective plug against fluid movement and at the same time supports the casing and formations. Based on the operation conditions, different types of additives are used to make the cement slurry, and incorporation of a new additive considerably affects all properties of the cement slurry and the solidified sheath. In this work, lab experiments were performed to investigate alteration of the Saudi Class G cement properties after incorporation of olive waste into the slurry, and the possibility of replacing the commercial retarder with olive waste was also studied in this work. Five samples with different olive waste content were prepared, and their rheological characteristics, thickening time, mechanical properties, and permeability were evaluated after 24 h of curing at 95 °C. The results indicated that olive waste could replace the use of a commercial retarder. The incorporation of olive waste did not affect the cement plastic viscosity, while the yield point, 10 s, and 10 min gel strengths of the cement were considerably increased with the increase in the olive waste content. The cement compressive strength was also increased with the incorporation of olive waste of a maximum of 0.375%, and the permeability decreased with the addition of a maximum of 0.25% olive waste.

Application of Organoclays in Oil-Based Drilling Fluids: A Review.

Organoclays (OCs), formed by surface modification of clay minerals using organic compounds, are typical additives for providing rheology for oil-based drilling fluids (OBDFs). There are different studies on the effect of OCs on the rheological properties of oil-based systems under high-pressure, high-temperature (HPHT) conditions, but finding new OCs as rheology control agents is attractive for drilling fluid engineers. This work reviews different OCs used in OBDFs, namely, organo-montmorillonite (OMMT), organo-sepiolite (OSEP), and organo-palygorskite (OPAL). Furthermore, the structure of OCs in OBDFs, their rheological properties, and the thermal stability of OCs were investigated. Besides, the role of fibrous and layered OCs in enhancing the rheological properties of OBDFs is illustrated. Finally, the synergistic use of different OCs to enhance the thermal stability and rheological properties of the OBDFs is presented. The study highlights research gaps and recommendations for research approaches and potential areas that need further investigation. The application of OCs in OBDFs is a wide field and has huge potential to be developed. The use of OCs in OBDFs will promote development in the oil and gas industry.

Data-driven models to predict shale wettability for CO2 sequestration applications.

The significance of CO2 wetting behavior in shale formations has been emphasized in various CO2 sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO2 wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson's correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R2 of 0.99, testing R2 of 0.98 and a validation R2 of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R2 of 0.99, testing R2 of 0.97 and a validation R2 of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R2 values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R2 of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high.

Data-Driven Framework for Real-time Rheological Properties Prediction of Flat Rheology Synthetic Oil-Based Drilling Fluids.

Appropriate mud properties enhance drilling efficiency and decision quality to avoid incidents. The detailed mud properties are mainly measured in laboratories and are usually measured twice a day in the field and take a long time. This prevents real-time mud performance optimization and adversely affects proactive actions. As a result, it is critical to evaluate mud properties while drilling to capture mud flow dynamics. Unlike other mud properties, mud density (MD) and Marsh funnel viscosity (MFV) are frequently evaluated every 15-20 min in the field. The goal of this study is to predict the rheological properties of flat rheology synthetic oil-based mud (SOBM) in real time using machine learning (ML) techniques such as random forest (RF) and decision tree (DT). A proposed approach is followed to first predict the viscometer readings at 300 and 600 RPM (R 600 and R 300) and then estimate the other mud properties using the existing equations in the literature. A set of data contained MD, MFV, and viscometer readings (R 300 and R 600) for different samples from the same mud type. The mud samples were collected after going through a shale shaker. MD and MFV are measured by a mud balance and a Marsh funnel, respectively, while rheology is evaluated using a viscometer. The data were randomly split into training, testing, and validation data sets. The ML models' performance was evaluated through average absolute percentage error (AAPE) and correlation coefficient (R). The proposed models predicted the viscometer readings as a middle stage with a low AAPE that did not exceed 4.5% for both models. The suggested models forecasted the rheological properties with a good degree of accuracy, with an AAPE being less than 7% for most of the parameters. The proposed models can save costs and time since there is no need to include additional tools in the rig location. Furthermore, these models will significantly aid in avoiding serious problems and achieving better rig hydraulics and hole cleaning, which in turn will technically and economically enhance drilling operations.