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This paper describes the utilization of produced and treated formation water for planting trees and growing algae in large ponds; in a massive scale in South Oman. A detailed study has been carried out to assess the injection requirements for pressure maintenance in the producing reservoir and using the remaining excess pot-treated water for farming of the palm trees. The produced water has been used as disposal in formations deeper than the producing horizons in the past. The produced water was separated in a processing station that received gross production from a number of fields in South Oman. This water was disposed in the aquifer underlying a producing reservoir that has experienced pressure maintenance due to this disposal. The impact of this excess water disposal on the aquifer was studied to evaluate the risk of breaching cap rock integrity. The risk was not significant but to ensure "no damage to the environment and people" it was decided to reduce or optimize injection rates to maintain the reservoir pressure safeguarding reserves. In addition, the disposal of the water required significant amount of power equivalent to emitting significant amount of CO2 annually just for water disposal. The study was carried out using simple material balance methods to predict the pressure behaviour given an injection profile. The recommendations from the study have already been implemented to convert the deep-water disposal to injection in the aquifer. This has been achieved by the integration of number of interfaces from sub-surface to field operations. All the pieces are in place to take it the next level of execution that is to treat the water at surface for oil removal, hence rendering the water at acceptable quality levels for tree plantation and algae ponds. The project also aims in a future second phase to further treat the water to higher specifications allowing the use of it for agricultural purposes. This would introduce a commercial farm that will depend on this source of water. This would be a novel concept in South Oman where the treated water will be used for farming solving multiple issues at multiple levels namely helping the business achieve its objective of sustained oil production, helping local communities with employment via farming and helping the organization care for the environment by reducing carbon footprints.

Objectives/Scope (25 - 50) PDO is in the process of transforming its well and urban planning by adopting digital technologies and Artificial Intelligence (AI) to improve organizational efficiency and maximize business value through faster quality decision. In 2020, PDO collaborated with a third-party contractor to provide a novel solution to an industry-wide problem: "how to effectively plan 100's of wells in a congested brownfield setting?". Business Transformation This paper describes an innovative AI-assisted well planning method that is a game-changer for well planning in mature fields, providing efficiency in urban and well trajectory planning. It was applied in one of PDO's most congested fields with a targeted infill of 43m well spacing. The novel well planning method automatically designs and optimizes well trajectories for 100-200 new wells while considering surface, subsurface and well design constraints. Existing manual workflows in the industry are extremely time consuming and sequential (multiple man-months of work) - particularly for fields with a congested subsurface (350+ existing wells in this case) and surface (limited options for new well pads). These conventional and sequential ways of working are therefore likely to leave value on the table because it is difficult to find 100+ feasible well trajectories, and optimize the development in an efficient manner. The implemented workflow has the potential to enable step change in improvements in time and value for brownfield well and urban planning for all future PDO developments. Innovation The innovative AI assisted workflow, an industry first for an infill development of this size, evaluates, generates and optimizes from thousands of drillable trajectories to an optimized set for the field development plan (based on ranked value drivers, in this case, competitive value, cost and UR). The workflow provides a range of drillable trajectories with multi-scenario targets and surface locations, allowing ranking, selection and optimization to be driven by selected metrics (well length, landing point and/or surface locations). The approach leads to a step change reduction in cycle time for well and urban planning in a complex brownfield with 100-200 infill targets, from many months to just a few weeks. It provides potential game-changing digital solutions to the industry, enabling improved performance, much shorter cycle times and robust, unbiased well plans. The real footprint and innovation from this AI-assisted workflow is the use of state-of-the-art AI to enhance team collaboration and integration, supporting much faster and higher quality field development decisions. Application of Technology This paper describes a novel solution to integrated well planning. This is a tangible example of real digital transformation of a complex, integrated and multi-disciplinary problem (geologists, well engineers, geomatics, concept engineers and reservoir engineers), and only one of very few applied use cases in the industry. This application also gives an example of "augmented intelligence", i.e. how AI can be used to truly support integrated project teams, while the teams remain fully in control of the ultimate decisions. The success of this approach leans on the integrated teamwork across multiple technical disciplines, not only involving PDO's resources, but also WhiteSpace Energy as a 3rd party service provider. The enhanced collaboration allowed all parties to highlight their constraints in an integrated way from the start, strengthening the technical discussion between disciplines and learning from each constraint impact and dependencies. (e.g. dog leg severity). In summary, the change in process flow moving from a sequential well planning and urban planning method to an iterative and fast AI solution – including all technical considerations from beginning represented for PDO an added value of over 6 months of direct cycle time HC acceleration.

Produced water is an inextricable part of the hydrocarbon recovery processes, yet it is by far the largest volume waste stream associated with hydrocarbon recovery. In a C-field in South Oman, the produced water has been disposed in the aquifer zone of the producing formation. The feasibility of alternative ways to dispose water at surface using alternative options is being evaluated with the objective of reducing (or completely stopping) this water disposal which has shown benefits in maximizing the recovery by reversing the pressure decline. A simple model has been used to quantify the benefits of produced water re-injection into the deep aquifer zone. Deep water disposal (DWD) has been on-going for over 20 years in the aquifer zone in the B-formation in this field in South Oman. All the produced water from the surrounding fields is sent for disposal near the field via the C-Field Processing Station DWD system. This DWD activity has provided important energy to the system as evident in the reversing reservoir pressure trend in field. However, due to various reasons, efforts are being put forward with the aim of replacing DWD with alternative ways of disposing produced water at surface. An integrated model has been built and calibrated to the field response and used to predict the field performance. The calibrated model recommends to continue pressure to the field through water disposal or injection system. The study predicts the complete discontinuation of DWD will put significant reserves at risk eroding the field value and has quantified the amount of water available for the alternative options for surface disposal. The study has also identified an opportunity to further optimize the solution for pressure maintenance and thereby, potentially improving the recovery from the field.

PDO is in the process of transforming its well and urban planning by adopting digital technologies and Artificial Intelligence (AI) to improve organizational efficiency and maximize business value through faster quality decision. In 2020, PDO collaborated with a third-party contractor to provide a novel solution to an industry-wide problem: "how to effectively plan 100's of wells in a congested brownfield setting?". This paper describes an innovative AI-assisted well planning method that is a game-changer for well planning in mature fields, providing efficiency in urban and well trajectory planning. It was applied in one of PDO's most congested fields with a targeted infill of 43m well spacing. The novel well planning method automatically designs and optimizes well trajectories for 100-200 new wells while considering surface, subsurface and well design constraints. Existing manual workflows in the industry are extremely time consuming and sequential (multiple man-months of work) - particularly for fields with a congested subsurface (350+ existing wells in this case) and surface (limited options for new well pads). These conventional and sequential ways of working are therefore likely to leave value on the table because it is difficult to find 100+ feasible well trajectories, and optimize the development in an efficient manner. The implemented workflow has the potential to enable step change in improvements in time and value for brownfield well and urban planning for all future PDO developments. The innovative AI assisted workflow, an industry first for an infill development of this size, evaluates, generates and optimizes from thousands of drillable trajectories to an optimized set for the field development plan (based on ranked value drivers, in this case, competitive value, cost and UR). The workflow provides a range of drillable trajectories with multi-scenario targets and surface locations, allowing ranking, selection and optimization to be driven by selected metrics (well length, landing point and/or surface locations). The approach leads to a step change reduction in cycle time for well and urban planning in a complex brownfield with 100-200 infill targets, from many months to just a few weeks. It provides potential game-changing digital solutions to the industry, enabling improved performance, much shorter cycle times and robust, unbiased well plans. The real footprint and innovation from this AI-assisted workflow is the use of state-of-the-art AI to enhance team collaboration and integration, supporting much faster and higher quality field development decisions. This paper describes a novel solution to integrated well planning. This is a tangible example of real digital transformation of a complex, integrated and multi-disciplinary problem (geologists, well engineers, geomatics, concept engineers and reservoir engineers), and only one of very few applied use cases in the industry. This application also gives an example of "augmented intelligence", i.e. how AI can be used to truly support integrated project teams, while the teams remain fully in control of the ultimate decisions. The success of this approach leans on the integrated teamwork across multiple technical disciplines, not only involving PDO's resources, but also WhiteSpace Energy as a 3rd party service provider. The enhanced collaboration allowed all parties to highlight their constraints in an integrated way from the start, strengthening the technical discussion between disciplines and learning from each constraint impact and dependencies. (e.g. dog leg severity). In summary, the change in process flow moving from a sequential well planning and urban planning method to an iterative and fast AI solution – including all technical considerations from beginning represented for PDO an added value of over 6 months of direct cycle time HC acceleration.