The process of drilling an oil or gas well is inherently challenging due to the unpredictable nature of many of the variables at play. However, the overall process can be broken down into many smaller phases, each of which has a signature of repeatability. By automating the repeatable processes, and by optimizing the inputs that are a function of variable conditions, we can industrialize the drilling process at a higher level and drive toward consistent, high performance. Improving operational inefficiency requires long-term training or special tools to help address downhole issues. This can be costly and requires many man hours. In the pilot reviewed in this paper, a Surface Automation Solution was implemented to improve well construction performance. The Surface Automation Solution was comprised of a Drilling Automation Platform (DAP), a real-time Intelligent Drilling Optimizer (IDO), an automated stick slip mitigation system, and Automation Lifecycle Management (ALM) supporting services. The Surface Automation Solution showed extraordinary performance, delivering efficient drilling connections, optimum drilling performance and mitigating drilling dysfunction. New records were achieved in every hole section where the system was operated, resulting in 51% overall ROP improvement compared to offsets, and 44% reduction in stick slip severity, translating to 3.2 rig days savings. In this paper, we will examine how the Surface Automation Solution saves well delivery time by automating drilling activities, mitigating drilling dysfunctions and optimizing parameters to increase ROP on each section. The outcome is measured by the performance, which in this case is time saved. The data shown is the overall macro key performance indicator (KPI) along with the performance at each individual hole section.

ADNOC is continuously enhancing its capabilities to manage its oil and fields efficiently by better planning, execution and operations that drives field development decisions, well performance, and safe operations. In this regard, ADNOC envisages to leverage the evolving Oil and Gas 4.0 technologies to enhance the well planning decisions of the sub-surface and drilling team through data-driven and AI methods. Effective well planning and operations require collaboration between different subsurface teams and drilling team leveraging multidisciplinary data, historical events and risks and constructing integrated drilling and sub-surface model for collaborative planning and keeping the model live. This requires having a live sub-surface model that is kept close to the field reality while reducing uncertainties. However, extracting key learnings, knowledge and experience from a variety of sources and reports is intense and requires lot of manual processing of data. An AI-based solution leveraging data analytics, natural language processing and machine learning algorithms is developed to automatically extract knowledge from a variety of data sources and unstructured data in building a live intelligent model that enables effective well planning, predicting operational hazards and plan mitigation. The solution systematically extracts, collects, validates, integrates, and processes a variety of data in different formats such as well trajectory, completion, historical events, risk offset well information, petrophysical data, geo-mechanical data, and technical reports. Newly acquired data comprising drilling events, geological and reservoir properties are integrated continuously to keep the model live and digital representation.

Significant mud losses during drilling often compromises well integrity whenever sustainable annular pressure (SAP), is observed due to poor cement integrity around 9-5/8-in casing in wells requiring gas lift completion. Heavy Casing Design (HCD) is applied as a solution; whereby, two casing strings are used to isolate the aquifers and loss zones, thus ensuring improved cement integrity around the 9 5/8-in intermediate casing. Casing While Drilling (CWD) is a potential solution to mitigate mud losses and wellbore instability enabling an optimized alternative to HCD by ensuring well integrity is maintained while reducing well construction cost. This paper details the first 12 ¼-in × 9-5/8-in non-directional CWD trial accomplished in Abu Dhabi onshore The Non-Directional CWD Technology was tested in a vertical intermediate hole section of a modified heavy casing design (MHCD) aimed at reducing well construction cost over heavy casing design (HCD) as shown in the figure 1. A drillable alloy bit with an optimized polycrystalline diamond cutters (PDC) cutting structure was used to drill with casing through a multi-formation interval with varying hardness and mechanical properties. Drilling dynamics, hydraulics and casing centralization analysis were performed to evaluate the directional tendency of the drill string along with the optimum drilling parameters to address the losses scenario, hole cleaning, vibration, and maximum surface torque. The CWD operation was completed in a single run with zero quality, health, safety, and environment (HSE) events and minimum exposure of personal to manual handling of heavy tubulars. Exceptional cement bonding was observed around the 9 5/8 in casing indicative of good hole quality despite running a significant number of centralizers (with smaller diameter), compared with the conventional drilled wells (cement bond logging was done after the section). CWD implementation saved two days of rig operations time relative to the average of the offset wells with the same casing design. The rate of Penetration (ROP) was slightly lower than the conventional drilling ROP in this application. The cost savings are mainly attributed to the elimination of casing-running flat time and Non-Productive Time (NPT) associated with clearing tight spots, BHA pack-off, wiper trips. The application of CWD in the MHCD wells deliver an estimated saving of USD 0.8MM in well construction cost per well compared to the HCD well design. Additional performance optimization opportunities have been identified for implementation in future applications. The combination of the MHCD and CWD technology enhances cementing quality across heavy loss zones translating into improved well integrity. Implementing this technology on MHCD wells could potentially save up to USD 200MM (considering 250 wells drilled). This is the first application of the technology in Abu Dhabi and brings key learning for future enhancement of drilling efficiency. The CWD technology has potential to enhance the wellbore construction process, which are typically impacted by either circulation losses and wellbore instability issues or a combination of both, it can applied to most of the offshore and onshore fields in Abu Dhabi.

The oil industry, in its constant strive to maximize gains out of operational data is constantly exploring new horizons where to combine the latest advances in data science and digitalization, into the areas where key decisions to drive economical and operational decisions reside with an aim at optimizing the capital expenditure through sound decision making. High volume operational data has been recognized as hiding many opportunities where the captured details these repositories that include real time logs and bit run summaries, provide a clear opportunity where to extract insights to support optimized decisions in terms of equipment selection to achieve the desired operational objectives. Current possibilities within data science have opened the possibilities through viable solutions, which in this case, aims at providing advise on which equipment in terms of BHA and Bits to select, that would yield the desired outcome for a drilling run. The whole exercise being based on evidence gathered from previous runs where the details for the equipment, the relevant well characteristics, and the observed rates of penetration and the used parameters, are taken into consideration to provide the optimum combination to be implemented in new runs. The present study describes the methodology in terms of data utilization, data science method development and solution deployment, with the associated issues that had to be addressed in order to provide a viable solution in terms of data utilization, technical validity and final user utilization, as well as a series of recommendations to be addressed within any such endeavors to assure the value addition.

This paper describes the use of Artificial Intelligence (AI) to support well planning in an Abu Dhabi offshore field. In this application, AI has been used for automated and unbiased evaluation of well trajectories, with the objective to optimize the cost, risk versus value trade-offs while considering complex issues such as anti-collision with existing wells. A Rapid Random Tree (RRT) algorithm, well known for applications in robotics, has been used to generate well trajectories for 2 actual drilling projects. The algorithm creates a full and unbiased option space of feasible well trajectories, presented in a custom-built and collaborative digital solution. Results demonstrate that AI-generated well trajectories were 2-5% shorter than manually planned and/or actual drilled wells. This use case also shows that an AI can design thousands of possible well trajectories in only a few hours, adhering to well design rules and anti-collision constraints. This would lead to significant time savings, and possibly material drilling cost reductions, in even more congested brownfield assets. This paper describes a real application of AI-assisted well trajectory planning in an operational setting, with a comparison to manually planned and actual drilled wells. As such, this provides a rather unique insight into the business value-adding potential of Artificial Intelligence in traditionally manual work processes.