Wellpad and Manifold Design with Collision Risk Considerations in Field Development Planning

Abstract

Field Development planning (FDP) of oil and gas fields is a multifaceted, challenging process due to the numerous decision variables involved. This process is further complicated by the environmental concerns, regulatory requirements, directional drilling, congested high-density fields, and various constraints, including design, spatial, operational, and cost. Additionally, onshore and offshore operations differ in terms of complexity and key components. These factors highlight the need for safe, optimal, environmentally conscious, and scalable planning.

This research aims to reduce the complexity of FDP by addressing both onshore and offshore settings and focusing on critical components. Onshore planning tackles detection of prohibited drilling areas, multi-well pad design with brownfield considerations, and anti-collision analysis with mitigations strategies. On the other hand, offshore planning addresses manifold and jumper design, among other complexities.

To ensure safe well placement, this research begins with the identification of prohibited drilling areas. U-Net deep learning architecture is employed to identify existing well pads as a baseline step toward the broader objective. Subsequently, multi-well pads are designed to minimize land use and increase operational efficiency and safety, especially with rise in directional drilling. The methodology identifies key well pad design parameters such as well spacing and orientation angle through analysis of various fields including but not limited to Marcellus Shale and Lower Eagle Ford Shale. To maintain wellbore integrity and safe drilling, anti-collision analysis is assessed. This analysis covers coordinate reference system, the International Geomagnetic Reference Field (IGRF), error model, ellipsoid of uncertainty, scanning method, directional uncertainty method, and separation factor formula. Additionally, collision mitigation strategies are proposed focusing on adjusting well pad parameters to minimize or eliminate collision risks during the planning process.

Detection of prohibited areas is evaluated using quantitative and visual analysis. The model converges to a combined loss (Dice loss + Binary Cross entropy) of 0.1 and demonstrates good generalization for unseen images in familiar fields, but it fails to generalize to completely new geographic regions.

The onshore results section for multi-well pad design and anti-collision analysis is divided into three parts which are a case study, evaluation of different configurations, and scalability analysis. The case study presents a detailed step-by-step process of multi-well pad design and anti-collision analysis. Furthermore, collision mitigation and brownfield scenario are incorporated demonstrating the adaptability of the design. The comparative analysis highlights the flexibility of the design and the impact of varying the well pad parameters on anti-collision analysis results, emphasizing the importance of selecting appropriate configurations. The scalability analysis confirms the efficiency of the workflow and its applicability to large fields.

In offshore planning, a structured and scalable methodology for subsea manifold design is presented, addressing numerous challenges including well arrangement, optimal manifold type selection and placement, manifold-to-pipeline connections, and jumper routing. The proposed methodology integrates various design variables, including pipeline type, jumper type and length, number of connected wells, and layout configurations. It places special focus on cluster manifold design and practical jumper routing. For distributed subsea layout, the jumper paths are generated using A* algorithm, with a repulsion model applied to resolve collisions. For flexible jumpers, a minimum bending radius is enforced to ensure design feasibility.

The approach is validated through examples, including a clustered subsea layout, a distributed subsea layout, and a layout featuring well satellites. Each case highlights its adaptability under different variables and architecture. Results showcase its effectiveness, as it minimizes rework and manual intervention. Additionally, it accounts for multiple constraints and automates key design steps enabling faster and more informed decision-making, which is critical to maintain speed, scalability, and operational feasibility.

Overall, this research presents comprehensive methodology for onshore and offshore FDP aiming to provide scalable and efficient processes that consider the broader context, interrelated constraints, and environmental concerns.