Leveraging Game AI to Transform Integrated Brownfield Well Planning

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.

Leveraging Game AI to Transform Integrated Brownfield Well Planning

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.

Drilling Fluids Project Engineering Guidance and Most Common Fluids Related Challenges for Deepwater and HPHT Offshore Wells

This paper discusses both technical and project management aspects of drilling fluids services for deepwater and high pressure high temperature (HPHT) offshore drilling projects. The technical discussion part includes deepwater and HPHT specific fluids related concerns such as logistics, narrow drilling window, shallow hazards, gas hydrates, HPHT conditions and low temperature rheology; together with practical solutions for each of them. As some of these challenges cannot be met by only fluids itself, technologies such as managed pressure drilling (MPD), dual-gradient drilling (DGD) and use of special downhole tools are also included in the discussions. The project management aspect is covered for both the planning and execution phases. A newly developed Four Stage Planning Guideline (4SPG) with a recommended timetable is proposed for high-profile offshore drilling projects. Starting from fluids selection to preparation of the contingency plans is discussed in detail for the planning phase. Execution phase is discussed mainly for service company representatives on how to follow main or contingency plans effectively and ensure good communication is achieved with all parties involved. Work model presented in this paper can be used as a complete guideline by operating and service company representatives in order to increase the success rate of these high-risk offshore drilling projects and ensure learnings are captured in a structured way for continuous improvement.

A COMPARISON BETWEEN 3D RECONSTRUCTION USING NERF NEURAL NETWORKS AND MVS ALGORITHMS ON CULTURAL HERITAGE IMAGES

In this research, an innovative comparison between 3D reconstructions obtained by means of Artificial Intelligence, in particular NeRF Neural Networks, and by Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) open-source algorithms is proposed. The 3D reconstruction comparison is performed on two test cases, one of cultural interest, one useful only for technical discussion. It is known that the approaches are traditionally used with different objectives and in different contexts but they can however also be used with similar purpose, i.e., 3D reconstruction. In particular, we were interested in evaluating how NeRF reconstructions are accurate from a metric point of view and how the models obtained from the application of NeRF differ from the model obtained from the classical photogrammetry. By analyzing the results in the considered test cases, we show how NeRF networks, although computationally demanding, can be an interesting alternative or complementary methodology, especially in cases where classical photogrammetric techniques do not allow satisfactory results to be achieved. It is therefore suggested to expand efforts in this direction by exploiting, for example, the numerous improvement proposals of the original NeRF network.

Can Quantum Correlations Lead to Violation of the Second Law of Thermodynamics?

Quantum entanglement can cause the efficiency of a heat engine to be greater than the efficiency of the Carnot cycle. However, this does not mean a violation of the second law of thermodynamics, since there is no local equilibrium for pure quantum states, and, in the absence of local equilibrium, thermodynamics cannot be formulated correctly. Von Neumann entropy is not a thermodynamic quantity, although it can characterize the ordering of a system. In the case of the entanglement of the particles of the system with the environment, the concept of an isolated system should be refined. In any case, quantum correlations cannot lead to a violation of the second law of thermodynamics in any of its formulations. This article is devoted to a technical discussion of the expected results on the role of quantum entanglement in thermodynamics.

Query Optimization in a Blockchain-Based Land Registry Management System

Digitization of land records is not sufficient for preventing fraud cases, time delay, and brokers' involvement. Distributed Ledger Technology (DLT) is used for making this digitized record more secure and process it in a decentralized way, and reduces paperwork in selling and buying of land. Blockchain technology has come to the fore in recent years and is the center of technical discussion, with intuitive applications driven by its network architecture. It has been firmly established as one of the most important emerging technologies. This article aims to implement a land registry mechanism using blockchain technology and optimize searching of land records in blockchain. Interplanetary File System (IPFS) provides an infrastructure that offers a precise portrayal of all the members' roles. The application interacts with the blockchain network, which is built using IPFS. This paper will help in providing a secure and decentralized system for the land registry process. The proposed consensus algorithm MRRCM achieves less time required to generated a block on an average by 3.06% round-robin and 96.48% PoW approach. The proposed modified hash table search approach requires less time to search the blockchain's land record block than the extensive liner searches and hash table search approach. A search for a land record in the blockchain reduces the search time on an average by 59.5% compared to the traditional extensive liner search approach and by 18.68% as compared with the hash table search approach.

Data Exchange and Collaboration Realize Automated Drilling Control Potential

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 201763, “Exploiting the Full Potential in Automated Drilling Control by Increased Data Exchange and Multidisciplinary Collaboration,” by Kristian Gjerstad, SPE, and Ronny Bergerud, Sekal, and Stig Tore Thorsen, SPE, Equinor, prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5-7 October. The paper has not been peer reviewed. The complete paper describes challenges that must be overcome to reach the goal of drilling systems automation (DSA). The authors explore steps necessary to realize the full potential of performance-enhancing functionalities in automated drilling control (ADC) software, highlight current gaps, and present relatively easily achievable goals that can enable significant cost reduction and improvements in automation and safety. They also emphasize that automation is a multidisciplinary task, and that success requires collaboration between different sectors of the drilling industry. Overview The 19-page complete paper includes detailed technical discussion of topics ranging from the basic principles of an ADC system and practical challenges experienced with a model-based digital twin approach to suggested solutions and improvements. Each topic is divided into numerous related discussions. Because delving into each of these discussions is not possible in this synopsis, these have been outlined, with a few supporting points included for each. The Potential of ADC Systems Dedicated software applications - referred to by the authors as ADC systems - for protecting the well, increasing safety, automating repetitive operations, and optimizing the drilling process, have been available for some time. Several projects in which sophisticated ADC systems evaluate downhole conditions to assist the driller with judgments and decisions have been reported, with promising results including noticeable improvements in cost savings, reduced incidents, and improved safety. However, the number of rigs with sophisticated ADC systems running actively in real time is not high, and even on rigs where an ADC system is in use, the potential of the system generally is not fully leveraged. One reason is that these ADC systems are based on models of the drilling process running in parallel with the real process, with each requiring the exact same inputs in real time to work optimally. Many of these inputs are entered manually because the instrumentation, equipment, and infrastructure needed to automate the data transfer are not in place. The inputs that are automated may not be sufficiently accurate or reliable, so manual interactions are needed. Experience shows that even on relatively new rigs with modern instrumentation, a large untapped potential exists. An underlying reason for this lack of automated inputs is that different parties involved in establishing the required instrumentation and automated signal transfer are not well aligned. Thus, increased automation and repeatability can introduce increased staffing and cost for operating the ADC system. To overcome this paradox, better collaboration is required among the vendors in the complete production loop.

Access to Learning Through Mobiles

The common myth that mobile learning cannot propel in a rural setting is null and void. The influx of modern ICTs like mobile technologies can revolutionize information access among the less privileged in many African communities. Using the Actor-Network Theory as a methodological tool, the chapter explores opportunities of increasing knowledge access through mobiles, by understanding the networks involved in farmer's mobile learning practice, with reference to Uganda. The chapter reveals that mobile technologies offer affordable individual and group learning opportunities to smallholder farmers. Learning is a socially constructed activity, where farmers with access to ICTs like mobile phones share knowledge among those with no access. Through a socio-technical discussion, technological initiatives ought to be pro-people where farmers' needs are key considerations in the mLearning actor-network. For sustainable impacts, all actors need to work collaboratively, negotiate different realities, and appreciate the local challenges within which mobile technologies can support learning.