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With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.
With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.
High torque, friction factors, and pick up weights were major challenges encountered by a major operator in Abu Dhabi while planning to drill challenging extended reach development (ERD) wells with complex 3D profiles. Well torque and drag simulations showed that planned depths were not reachable with water-based muds. This paper describes the implementation of a mechanical lubricant, which resulted in significant decrease of the friction factors and turned an ERD well from not drillable to drillable with water-based mud. After analyzing several possibilities, the solutions were narrowed down to two: use either a new generation mechanical lubricant or a reservoir non-aqueous fluid (NAF). The complexity was amplified by the necessity to re-design a filter-cake breaker for NAF, were this option to be selected, due to the type of completion. This second option would also create a substantial cost increase for the operator for products and rig time; therefore, the decision was made to introduce a mechanical lubricant. A comprehensive study and lab tests were conducted to ensure compatibility and stability of the lubricant with a planned mud type at downhole conditions. The results of this study were promising enough for the operator to introduce this lubricant, aiming a substantial reduction in torque and drag to enable drilling of the longest horizontal section in the history of the project. Before addition of the mechanical lubricant, drilling continued with a conventional type of lubricant, noticing an increasing tendency of torque and drag tracking the predicted trends. At a certain stage, drillstring buckling was observed and drillpipe started to reach their limits. To mitigate these impediments, the mechanical lubricant was introduced into the drilling fluid. After reaching the optimum concentration, the mechanical lubricant eliminated buckling and provided significant reduction in torque, pick-up, and slack-off friction factors, respectively by 27%, 52%, and 42%. These parameter improvements facilitated continued drilling the well to final depth without reaching the drillpipe limits. Additionally, the well and bottomhole assembly (BHA) designs allowed for significant margins in case of a stuck pipe event, and based on the new friction factors, the well could be extended by 3,000 ft without reaching the drillpipe limits. The impact of this exercise in future ERD wells is considerable. It will simplify well and completion designs, improve logistics by reducing the amount of chemical movements, facilitate drilling fluids selection, and optimize the well cost. The paper covers the gaps related to drilling complex ERD wells with water-based drilling fluids. It provides detailed methods and procedures covering the suitable application of the mechanical lubricant and the extensive laboratory tests done during the planning stage, as well as the field application and results. The proposed solution can be used during the well planning process in any other area of the world.
"Motivation is the catalysing ingredient for every successful innovation." Clayton Christensen. One of the pillars of project management is motivation. The success of any organisation rests on the ability of a leader to identify the motivation factors of a team and to encourage everyone to maintain positive thoughts and behaviour to achieve challenging targets successfully. This paper explains the practices implemented in the project that increased the motivation level of the team during the difficult and uncertain times of the oil downturn. This method consisted of three main phases. First, to conduct an initial survey to understand the motivation level of the team and identify the areas of improvement. Based on the results of the first phase, a master plan was created it to tackle the areas of improvement and lead the team to achieve the organisations annual objectives. To create the plan, it was necessary to identify the unique strengths and passions of every member of the team, address the need for recognition and strengthen the sense of belonging. After executing the master plan, a final survey was conducted to measure the success of the implementation. The results were outstanding in several areas. With the results of the final survey, it confirmed that the team's motivation level improved by 12%, and in some areas, such as recognition and belonging in 34%. Besides the statistics, the improvement in the motivation level resulted in a more creative team that was able to develop more than thirty operational initiatives that brought significant savings to the customer. All the challenging key performance objectives were achieved contributing to the company's business success. In conclusion, it was proven that even during the uncertain and challenging times of the oil industry, if we can keep our team motivated by reducing the weaknesses and building on top of the team's strengths, recognizing people's contribution to the company and showing them that their work clearly contributes to the business, and will always be possible to achieve even the most challenging targets. The approach is innovative in the sense that goes away from traditional financial incentive plans based on monetary rewards and looks at a deeper and more meaningful aspect of the human been regarding motivation. The methodology is based on Maslow's pyramid and Ikigai concept applied during the most challenging times in the oil industry; resulted in a boost in team motivation and overachievement of challenging key performance objectives.
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