To ensure an effective drilling operation of an explored well, the associated hydraulics program should be established carefully based on the correct prediction of a drilling fluid’s pressure drop and velocity field. For that, the impact of the drill string orbital motion should be considered by drilling engineers since it has an important influence on the flow of drilling fluid and cuttings transport process. In the present investigation, the finite volume method coupled with the sliding mesh approach is used to analyze the influence of the inner cylinder orbital motion on the flow of a power-law fluid (Ostwald-de Waele) in an annular geometry. The findings indicate that the orbital motion positively affects the homogeneity of the power-law axial velocity through the entire eccentric annulus; however, this impact diminishes as the diameter ratio increases. In addition, higher torque is induced when the orbital motion occurs, especially for high values of eccentricity and diameter ratio; nonetheless, a slight decrease in torque is recorded when the fluid velocity increases.

The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the main reason for bottom hole assembly (BHA) tool failure or excessive wear. This paper aims to maximize the ROP while managing the torque to keep the depth of cut within an acceptable range during the cutting process. To achieve this, machine learning algorithms are applied to build ROP and drilling torque models. Then, a metaheuristic algorithm is used to determine the optimal technical control parameters, the weight on bit (WOB) and revolutions per minute (RPM), that simultaneously enhance the ROP and mitigate excessive vibrations. This paper introduces a new methodology for mitigating drill string vibrations, improving the rate of penetration (ROP), minimizing BHA failures, and reducing drilling costs.

The effective determination of pressure losses depends on accurate knowledge of the drilling fluid rheology. As the fluid circulates deeper around the wellbore, its rheological behavior undergoes significant alterations due to the variations in downhole conditions encountered. The present study investigates the effects of the rheological properties of Yield-power law fluid at various pressures and temperatures on annular pressure losses and velocity profiles. Simulations were performed using Computational Fluid Dynamics to examine the fluid flow in turbulent and laminar regimes. Comparison between numerical, experimental and slot approximation model results showed a good agreement. Results indicated that pressure losses have reduced in both regimes with increasing temperature, at a constant pressure. However the pressure has the opposite effect at a constant temperature. For a drilling fluid flow velocity of 1 m/s, the elevation of temperature from 25 °C to 90 °C, decreases the pressure drop gradient by (31% to 48%) at low and high- pressure conditions respectively. Whereas, the influence of increasing pressure on pressure losses is more apparent at 25 °C. Earlier transition from laminar to turbulent is observed with temperature rise. Therefore, the temperature effect on pressure losses in the turbulent region; is shown for different Generalized Reynolds numbers.

Coiled tubing (CT) plays a pivotal role in oil and gas well intervention operations due to its advantages, such as flexibility, fast mobilization, safety, low cost, and its wide range of applications, including well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject to fatigue and mechanical damage caused by repeated bending cycles, internal pressure, and environmental factors, which can lead to premature failure, high operational costs, and production downtime. With the development of CT properties and modes of application, traditional fatigue life prediction methods based on analytical models integrated in the tracking process showed, in some cases, an underestimate or overestimate of the actual fatigue life of CT, particularly when complex factors like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a dataset derived from both lab-scale and full-scale fatigue tests. We incorporated the impact of different parameters such as CT grades, wall thickness, CT diameter, internal pressure, and welding types. By using advanced machine learning techniques such as artificial neural networks (ANNs) and Gradient Boosting Regressor, we obtained a more precise estimation of the number of cycles to failure than traditional models. The results from our machine learning analysis demonstrated that CatBoost and XGBoost are the most suitable models for fatigue life prediction. These models exhibited high predictive accuracy, with R² values exceeding 0.94 on the test set, alongside relatively low error metrics (MSE, MAE and MAPE), indicating strong generalization capability. The results of this study show the importance of the integration of machine learning for CT fatigue life analysis and demonstrate its capacity to enhance prediction accuracy and reduce uncertainty. A detailed machine learning model is presented, emphasizing the capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable CT management and safer, more cost-efficient well intervention operations.

O mapeamento 3D da resistência das rochas desempenha um papel crucial na otimização da perfuração, fornecendo informações detalhadas sobre as propriedades geomecânicas do subsolo, que são essenciais para a otimização da perfuração. Ao criar uma representação tridimensional das variações de resistência da rocha, os engenheiros podem identificar áreas de alta e baixa resistência, permitindo-lhes adaptar técnicas e equipamentos de perfuração a condições geológicas específicas. Esta precisão reduz o risco de falha do equipamento, minimiza a probabilidade de instabilidade do poço e aumenta a eficiência operacional geral. Além disso, compreender a distribuição da resistência das rochas ajuda a prever riscos potenciais de perfuração, como fraturas e falhas, permitindo uma tomada de decisão proativa que pode economizar tempo e recursos. Em última análise, o mapeamento 3D da resistência das rochas não só melhora o desempenho e a segurança da perfuração, mas também contribui para uma extração de recursos mais econômica, otimizando a colocação dos poços e reduzindo o tempo não produtivo durante as campanhas de perfuração. Este estudo apresenta uma abordagem para prever a resistência das rochas no espaço tridimensional usando métodos geoestatísticos. Utilizamos um conjunto de dados, incluindo propriedades geológicas e mecânicas obtidas de vários furos de sondagem no campo. Ao empregar técnicas geoestatísticas avançadas, particularmente ponderação de distância inversa e krigagem ordinária, desenvolvemos um modelo preditivo que interpola espacialmente os valores de resistência da rocha em toda a área modelada.