N. J. Isuk

In the present-day oil and gas industry, the chemicals commonly employed as pH controllers in drilling operations are largely imported at exorbitant costs. These high costs contribute significantly to the overall drilling expenditure and create rippleeffects on the national economy. Hence, there is a pressing need to identify and develop locally available substitutes that are both cost-effective and environmentally friendly. This study focuses on investigating the suitability of burnt coconut fibre, a readily available agricultural by-product in Nigeria, as a pH enhancer in drilling mud formulations. The research methodology involved the preparation of water-based mud samples treated with different concentrations of coconut fibre solution, alongside conventional additives such as sodium hydroxide (NaOH) for comparison. Laboratory analyses were conducted to determine pH variation, rheological properties, and mud density under controlled conditions. The performance of the coconut fibre was evaluated based on its ability to increase and stabilize mud alkalinity while maintaining desirable drilling fluid characteristics. The results revealed that burnt coconut fibre imparted a significant pH value of approximately 13.0 in the drilling mud, which is comparable to the 13.8 obtained with sodium hydroxide. Additionally, the coconut fibre showed potential in enhancing rheological properties, such as yield point and gel strength, while also exhibiting ecofriendly and biodegradable characteristics. These findings demonstrate that coconut fibre can serve as a viable supplement to imported chemical additives, thereby reducing dependency on foreign products, lowering drilling costs, and supporting sustainable resource utilization

The oil and gas industry is extremely risky and difficult, necessitating the safe and cost-effective execution of all operations. A drilling operation's success depends on the careful selection and application of drilling fluid. Investigating how contaminants affect the properties of water-based drilling fluids is the main goal of this study. This experiment revealed that the fluid loss into the formation was enhanced when sodium salt was present in the mud system. Additionally, as the mass of the mud sample increased from 1g to 5g, the apparent viscosity and gel strength increased, but the plastic viscosity and pH stayed constant. On the other hand, the yield point showed minimal growth. Since the amount of cement sample used was increased from 1g to 5g while the pH remained constant, all rheological properties of the mud increased significantly when cement was used as a contaminant. The carbonate effect is largely on the Gel strength which decreased as the amount of added carbonate increased. The pH had no charges, which also meant carbonate kept the mud in its alkaline state, as it was the case with cement. In conclusion, the presence of a contaminant on the drilling mud either reduces or increases the rheological properties of the mud sample. This in turn affects the rate of penetration, its performance and also could pose serious drilling problems.

This research presents a comprehensive systematic review of artificial intelligence (AI) techniques and algorithms employed in predictive analytics for drilling hazard management, specifically focusing on stuck pipe incidents, lost circulation events, and wellbore instability. Drilling hazards collectively account for 30-40% of non productive time (NPT) in global drilling operations, costing the oil and gas industry approximately $8-12 billion annually. Traditional monitoring systems rely on reactive, empirical approaches that fail to provide early warnings, while modern drilling operations generate 1-2 terabytes of data per well, creating opportunities for AI-based predictive solutions. Through systematic analysis of 78 peer-viewed research papers published between 2010-2024, this study evaluates the performance characteristics, implementation challenges, and economic viability of various AI algorithms including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, ensemble methods, and deep learning approaches. The research reveals a clear performance hierarchy among AI methods, with deep learning achieving the highest accuracy rates (90-97%) but requiring substantial computational resources and datasets exceeding 50,000 examples. Traditional neural networks demonstrate optimal balance between performance (88-94% accuracy) and practicality, making them the most widely adopted approach in commercial implementations.