Leveraging Game AI to Transform Integrated Brownfield Well Planning

2021 ◽  
Author(s):  
Fakhriya Shuaibi ◽  
Mohammed Harthi ◽  
Samantha Large ◽  
Jane-Frances Obilaja ◽  
Mohammed Senani ◽  
...  

Abstract 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.

2021 ◽  
Author(s):  
Fakhriya Shuaibi ◽  
Mohammed Harthi ◽  
Samantha Large ◽  
Jane-Frances Obilaja ◽  
Mohammed Senani ◽  
...  

Abstract 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.


Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 141
Author(s):  
F. J. G. Silva ◽  
M. R. Soares ◽  
L. P. Ferreira ◽  
A. C. Alves ◽  
M. Brito ◽  
...  

The structure of car seats is becoming increasingly complex, with mixing of wire conformation and plastic injection. The plastic over-molding process implies some labor, which can be reduced if novel solutions are applied in this manufacturing area. The handling of the wires used in car seats is the main problem identified in the process, wasting time both in the feeding and in the extraction of the molds used in the wire over-molding process. However, these machines are usually extremely compact and the free space around them is too short. In classic molding injection machines, there are just two half-molds, the female, and the male. In the over-molding process of wires used in car seats, three half-molds are used in order to increase the cycle time. Thus, to solve this problem, the classic robotic solutions are not appliable due to lack of space and elevated cost. This work describes the development of an automated solution able to handle the wires in both the feeding and the extracting phases of the production cycle, avoiding the traditional labor costs associated with this type of machine. Departing from an industrial need, the developed novel solution is described in detail and can be successfully adapted to other situations of low added-value products where it is needed to increase the productivity and competitiveness of the product. The system developed uses mechanical and pneumatic solutions which, combined, can be used to solve the identified problem, occupying a restricted space and requiring a small budget. This solution can be translated into guidelines that will allow the analysis of situations where the same system can be applied.


2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.


2018 ◽  
Vol 108 (11-12) ◽  
pp. 796-801
Author(s):  
G. Schuh ◽  
C. Kelzenberg ◽  
J. Wiese ◽  
F. Stracke

Der Werkzeugbau kann bei der Produkteinführung entscheidend zu mehr Effizienz in Bezug zu Kosten und Zeit beitragen. Hinsichtlich der Optimierung der werkzeugbauseitigen Wertschöpfung birgt besonders der Serienanlauf der Werkzeuge aufgrund hoher Zeit- und Kostenanteile umfangreiches Potenzial. Die digitale Prozessunterstützung stellt in diesem Zusammenhang einen maßgeblichen Optimierungsansatz dar. Neben einer verbesserten Prozesssynchronisation lassen sich Lerneffekte standort- und projektübergreifend nutzen.   Tool making plays a decisive role in the realization of time and cost advantages of new products. With regard to the optimization of the tool shop’s added value, the qualification and ramp-up of tools holds extensive potential due to high time and cost shares. In this context, digital process support represents a decisive optimization approach. In addition to improved process synchronization, learning effects can be used across different sites and projects.


2019 ◽  
Vol 56 (4) ◽  
pp. 1237-1262
Author(s):  
Lin Zhang ◽  
Pieter Hooimeijer ◽  
Yanliu Lin ◽  
Stan Geertman

Public participation in urban planning is a contested issue in China. In this article, we look at the endogenous mechanism of institutional change, by analyzing the roles and motivations of “third-party” planning professionals in two contrasting cases: a government-led and a citizen-led participatory practice. Findings show that planners were advocates of citizen participation in heritage preservation in both cases and acted as “mediators” in the first and “activists” in the second, yet remained within the mainstream planning structure. Their motivation to serve the rights of the citizens was clear, but subordinate to the drive to conform to the professional norms of authenticity in preservation in both cases. In contrast to both the Global North where more agonistic approaches question inclusive planning and the Global South where insurgent planning finds space to maneuver, Chinese urban planning seems to proceed by taking small steps within narrow margins when it comes to citizen engagement.


Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 599
Author(s):  
Yuan Liu ◽  
Licheng Wang ◽  
Xiaoying Shen ◽  
Lixiang Li

Dual receiver encryption (DRE), being originally conceived at CCS 2004 as a proof technique, enables a ciphertext to be decrypted to the same plaintext by two different but dual receivers and becomes popular recently due to itself useful application potentials such secure outsourcing, trusted third party supervising, client puzzling, etc. Identity-based DRE (IB-DRE) further combines the bilateral advantages/facilities of DRE and identity-based encryption (IBE). Most previous constructions of IB-DRE are based on bilinear pairings, and thus suffers from known quantum algorithmic attacks. It is interesting to build IB-DRE schemes based on the well-known post quantum platforms, such as lattices. At ACISP 2018, Zhang et al. gave the first lattice-based construction of IB-DRE, and the main part of the public parameter in this scheme consists of 2 n + 2 matrices where n is the bit-length of arbitrary identity. In this paper, by introducing an injective map and a homomorphic computation technique due to Yamada at EUROCRYPT 2016, we propose another lattice-based construction of IB-DRE in an even efficient manner: The main part of the public parameters consists only of 2 p n 1 p + 2 matrices of the same dimensions, where p ( ≥ 2 ) is a flexible constant. The larger the p and n, the more observable of our proposal. Typically, when p = 2 and n = 284 according to the suggestion given by Peikert et al., the size of public parameters in our proposal is reduced to merely 12% of Zhang et al.’s method. In addition, to lighten the pressure of key generation center, we extend our lattice-based IB-DRE scheme to hierarchical scenario. Finally, both the IB-DRE scheme and the HIB-DRE scheme are proved to be indistinguishable against adaptively chosen identity and plaintext attacks (IND-ID-CPA).


Author(s):  
A. A. Zelensky

The construction of a high-speed industrial real-time network based on FPGA (Field-Programmable Gate Array) for the control of machines and industrial robots is considered. A brief comparative analysis of the performance of the implemented Ethernet-based Protocol with industrial protocols of other leading manufacturers is made. The aim of the research and development of its own industrial automation Protocol was to reduce the dependence on third-party real-time protocols based on Ethernet for controlling robots, machines and technological equipment. In the course of the study, the requirements for the network of the motion control system of industrial equipment were analyzed. In order to synchronize different network nodes and provide short exchange cycle time, an industrial managed switch was developed, as well as a specialized hardware controller for processing Ethernet packets for end devices, presented as a IP-core. A key feature of the developed industrial network is that the data transmission in it is completely determined, and the exchange cycle time for each of the network devices can be configured individually. High efficiency and performance of implemented network devices became possible due to the use of hardware solutions based on FPGAs. All solutions described in the article as part of a modular digital system have been successfully tested in the control of machines and industrial robot. The results of field tests show that the use of FPGAs and soft processors with specialized peripheral IP-blocks can significantly reduce the tact of managing industrial equipment through the use of hardware computing structures, which indicates the promise of the proposed approach for solving industrial automation tasks.


Author(s):  
Paolo Ricci ◽  
Renato Civitillo

This research work aims to highlight social reporting and accountability system in Italian universities. After cases analysis and content analysis, we provide an overview of the state-of-the-art of accountability in academic research and education. In this perspective, a brief analysis of the most relevant literature regarding the topic is finalized to address and to compare Italian experiences of social reporting. Effective accountability systems can indeed turn from tools into goals in public administrations, and in doing so reporting takes on a completely different meaning: it is a contribution to the social added value created by the university, an extra obligation to take towards stakeholders, a further service to engage in to strengthen democracy. The culture of accountability should be introduced and guided mainly by law, with legal requirements about deadlines, tools and goals, and supervised by third-party authorities. Further work is still needed to fully grasp measurement complexities and the potential lying in the evaluation of academic performance – especially with relation to sociality and sustainability – that plays an important role in national and international ranking systems.


Author(s):  
T. Preethi Latha ◽  
K. Naga Sundari ◽  
S. Cherukuri ◽  
M. V. V. S. V. Prasad

<p><strong>Abstract.</strong> Now-a-days, collecting accurate and meaningful information about the urban localities/environment with the maximum efficiency in terms of cost and time has become more relevant for urban, rural and city level development planning and administration. This work presents a technical procedure for automatic extraction of building information and characterization of different urban building types within the Andhra Pradesh Capital Region Development Authority (APCRDA) jurisdiction areas using UAVs. The methodology consists of a number of sequential processes of acquisition and generation of high resolution Orthomosaic images, creation of 3D point cloud data, and image classification algorithm for feature extraction using exclusively the geometric coordinates. The main parameters of the urban structures/buildings assessed in this work are site area of the building, built-up area, and building dimensions, building setbacks and building height. Different geometric and appropriate metrics were automatically extracted for each of the elements, defining the urban typology. In this study, residential and commercial buildings were considered for the analysis and the measurements from Drone were validated with respective approved plans and manual inspections and showed positive results with threshold parameters like setbacks and height as per building bye-laws of Andhra Pradesh Government Order (G.O) 119. Based on the results, measurements from Drone are used for the buildings occupancy permissions following the State government building rules. This automated system would replace physical inspections and manual reports and significantly reduce costs and improve efficiency. As an important component in this pilot study, visualisation of the building information were represented / displayed on a web application in an interactive mode. This added value of UAV technology with an automated system in comparison with traditional ways provides geospatial information and can also be considered as an essential Earth Observation indicator which has the potential to lead to next generation Urban Information Services and in the Smart cities development. The considerable potential use of these indicators in urban planning and development offers an opportunity in appropriate decision making in day to day urban planning measures.</p>


2021 ◽  
Author(s):  
Óscar Brito Fernandes ◽  
Erica Barbazza ◽  
Damir Ivanković ◽  
Tessa Jansen ◽  
Niek Klazinga ◽  
...  

Abstract Background The launch in 2017 of the Irish 10-year reform programme Sláintecare represents a key commitment in the future of the health system. An important component of the programme was the development of a health system performance assessment (HSPA) framework. In 2019, the Department of Health of Ireland (DoH) and Health Service Executive (HSE) commissioned the technical support of researchers to develop an outcome-oriented HSPA framework, which should reflect the shared priorities of multiple stakeholders, including citizens. This study describes the method applied in the Irish context and reflects on the added value of using a citizen panel in the co-production of an HSPA framework. Methods A panel of 15 citizens was convened, recruited by a third-party company using a sampling strategy to achieve a balanced mix representing the Irish society. Panelists received lay-language preparatory materials prior to the meeting. Panelists used a three-color scheme to signal the inclusion and importance of performance measures. An exit questionnaire was administered to understand how participants experienced being part of the panel. The citizen panel was the first in a series of three panels towards the development of the HSPA framework, followed by panels including representatives of the DoH and HSE, and representatives from professional associations and special interest groups. Results The citizen panel generated 249 health performance measures ranging across 13 domains. Domains assessed as the most important included people-centeredness, coordination of care, and coverage. Prioritization of domains differed between panels. Citizen panelists shared a similar understanding of what a citizen panel involves and described their experience at the panel as enjoyable, interesting, and informative. Conclusions The engagement of citizens early on in the co-production process of the HSPA framework shaped the processes that followed, with the restating of priorities of the citizen panel informing decision-making throughout. Citizen engagement in HSPA development is essential for realizing value-based people-centered health systems and assuring an inclusive process that helps to generate trust and ownership of performance intelligence. Future research could expand on how citizen panels could be further engaged in co-creating mechanisms to assess, monitor, and report on the performance of health care systems.


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