Concept Selection Stage Research Articles

Digital Twin Approach Integrates Existing Facilities, Proposed Developments

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215074, “Digital Twin for Development and Subsurface Integration: A Window Into the Future,” by Thomas K.G. Gan, Elizabeth Sumadh, and Bhaminie Sagram, Shell. The paper has not been peer reviewed. _ The digital twin (DT) approach provides a virtual and digital representation of existing assets, spanning everything from onshore facilities to offshore platforms, pipelines, and subsurface data, all in one location. It has been used for the operator’s flagship Manatee project to develop three stacked reservoirs offshore Trinidad and Tobago. The DT was used to identify design modifications required at this onshore site and visualize proposed offshore assets. It integrates existing facilities and new design data, including site survey, subsurface, and well data to optimize well planning. Introduction The Manatee field lies within the East Coast Marine Area of Trinidad in approximately 92 m of water and consists of stacked reservoirs of clean and thin-bed unconsolidated sands. Production from these wells is expected to be processed through a new offshore platform and transported by the new pipeline to the Beachfield onshore facility. For a feasibility study and concept-selection stages, the DT platform played a crucial factor in visualizing and contextualizing development options and allowed front-end engineering contractors and operator teams to collaborate across multiple countries and inspect the location virtually. All engineering drawings, maps, design documents, and 3D models were powered by the DT platform. Key Challenges Traditionally, digital collaboration is performed through data and model export from different technical applications and results presented in slides and reporting documents. For oil and gas capital projects, typically 3–6 years elapse from feasibility stage to execution, deepening the issues caused by faulty corporate memory retention, staff movement, and project handover from different internal organizations. Manatee project teams were based in various locations in Trinidad, the US, the UK, the Netherlands, and India, thereby increasing the likelihood of error by using multiple sources of data. Furthermore, effective collaboration without a single source of truth of data can be an obstacle. Given that multiple software was used for model building, development-well planning, and satellite imagery, an inability exists to view 2D and 3D imagery of onshore and offshore facilities with the subsea and subsurface in one place. Furthermore, between 2020 and 2022, the COVID-19 pandemic led to imposed restrictions and limitations placed on site visits. In such scenarios, drone images and video data can prove useful to access facilities remotely. These challenges needed to be addressed by access to a centralized platform that collected, stored, and provided virtual representations to ensure continuous monitoring and efficient decision-making.

Innovation and Technological Adaptation of Business Actors in the Digital Age: A Digital Sociology Perspective

The advancement of information technology has changed many aspects of human life. Technology has entered various spaces of human life, so technology and humans are an integral unit. In the modern economic sector, the market has transformed business digitalization. This is both a challenge and an opportunity for business people. For this reason, this study aims to analyze the space for innovation and technological adaptation of business actors in responding to advances in information technology. In addition, this research also intends to outline the relevance of developing digital sociology studies. A qualitative approach through field studies and literature review is the method of choice for this research. Operationally, the research was conducted through two stages, namely the context determination stage and the concept selection stage. These two processes are in line with the principle of inductive logic in qualitative research which prioritizes empirical narratives and then develops conceptual narratives. This is intended to obtain a comprehensive understanding of the facts and theories about technological innovation and adaptation in the business world. The results show that digital technology has become a major catalyst for improving operational efficiency, expanding market reach, and creating competitive differentiation. Therefore, digital technology adaptation is not just an option but has become a necessity in an increasingly digitally connected era. Digital technology adaptation is one of the keys to success in a world that is undergoing a massive transformation from real space to digital space.

Intelligent Deepwater Solutions Hub Maximizes Value Across Asset Life Cycle

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 211453, “Intelligent Deepwater Advanced Solutions Hub for Value Maximization Throughout Asset Life Cycle,” by Sam Ryu, Carmela Chaney, and Wei Xu, ExxonMobil, et al. The paper has not been peer reviewed. _ Challenges posed by life-cycle management of an offshore asset, multidisciplinary systems engineering, and competing project objectives provide an opportunity for a new approach to maximize value. The objective of the complete paper is to introduce the operator’s intelligent Deepwater Advanced Solutions Hub (iDASH) approach, wherein various engineering tools are integrated strategically to improve decision quality. Introduction The complexities inherent in producing offshore oil and gas mean that creation of an optimal solution for all phases of the project life cycle is a serious challenge. Market research has suggested that the oil and gas industry could benefit from increased systems engineering with an approach that looks beyond first application, integrates requirements, and supports the identification of changes for new applications. The industry has been slow to adopt digitalization, with one of the lowest levels of digital maturity compared with other sectors. For the purposes of this paper, offshore oil and gas development can be broken into four phases after a field is evaluated to be commercially viable: concept selection, definition and execution, operations, and decommissioning. Each phase has unique objectives and challenges. During concept selection, generation of creative multiple alternatives for deepwater full-field development is a challenge because each offshore project is unique and requires time-consuming discussion cycles involving high uncertainty, multidisciplinary input, and constraints identified from a wide range of technical specialties. Once detailed design and execution begin, the objective becomes value preservation. Collaboration between the operator’s project management and contractors becomes a resource-intensive process. After startup, the objective moves toward value realization. Handover from project teams to operations can be time-consuming, leaving key pieces of data missing. Unexpected downtime, integrity issues, or process conditions can erode value and delay project payback. Operations also present an opportunity to process and implement feedback and lessons learned. The authors divide engineering tools into four pillars within the discussed iDASH approach: iConcept (concept-selection tools), iDesign (definition and execution tools), iOperate (operation tools), and iDecom (decommissioning tools), offering different value to each stage of value maximization, reducing capital expenditure (CAPEX) and maximizing net present value (NPV), achieving balance with other important objectives. These four pillars are expanded upon and illustrated with use cases in the complete paper. Finally, strong data integration is the foundation of this platform, enhancing interdisciplinary work, ideation cycle time, and decision traceability. Making Quality Decisions With Multiple Alternatives iConcept is a digital suite focusing on addressing challenges at the concept-selection stage. The centralized suite includes a variety of different tools developed both internally and externally based on data analytics, artificial intelligence (AI), and decision science that aim to improve decision quality. One of the key tools being leveraged is called the Field Layout Concept Optimizer. Through its use, a team can evaluate multiple alternative deepwater field developments rapidly to enable quick turnaround for quality decision-making. Multiple disciplines are integrated to reduce human bias.

Favoring Complexity: A Mixed Methods Exploration of Factors That Influence Concept Selection When Designing for Additive Manufacturing

Abstract The capabilities of additive manufacturing (AM) open up designers’ solution space and enable them to build designs previously impossible through traditional manufacturing (TM). To leverage this design freedom, designers must emphasize opportunistic design for AM (DfAM), i.e., design techniques that leverage AM capabilities. Additionally, designers must also emphasize restrictive DfAM, i.e., design considerations that account for AM limitations, to ensure that their designs can be successfully built. Therefore, designers must adopt a “dual” design mindset—emphasizing both, opportunistic and restrictive DfAM—when designing for AM. However, to leverage AM capabilities, designers must not only generate creative ideas for AM but also select these creative ideas during the concept selection stage. Design educators must specifically emphasize selecting creative ideas in DfAM, as ideas perceived as infeasible through the traditional design for manufacturing lens may now be feasible with AM. This emphasis could prevent creative but feasible ideas from being discarded due to their perceived infeasibility. While several studies have discussed the role of DfAM in encouraging creative idea generation, there is a need to investigate concept selection in DfAM. In this paper, we investigated the effects of four variations in DfAM education: (1) restrictive, (2) opportunistic, (3) restrictive followed by opportunistic (R-O), and (4) opportunistic followed by restrictive (O-R), on students’ concept selection process. We compared the creativity of the concepts generated by students to the creativity of the concepts they selected. The creativity of designs was measured on four dimensions: (1) uniqueness, (2) usefulness, (3) technical goodness, and (4) overall creativity. We also performed qualitative analyses to gain insight into the rationale provided by students when making their design decisions. From the results, we see that only teams from the restrictive and dual O-R groups selected ideas of higher uniqueness and overall creativity. In contrast, teams from the dual R-O DfAM group selected ideas of lower uniqueness compared with the mean uniqueness of ideas generated. Finally, we see that students trained in opportunistic DfAM emphasized minimizing build material the most, whereas those trained only in restrictive DfAM emphasized minimizing build time. These results highlight the need for DfAM education to encourage AM designers to not just generate creative ideas but also have the courage to select them for the next stage of design.

Environmental assessment in the conceptual design phase of new product development

Many studies have shown the importance of incorporating environmental issues into the process of new product development (NPD). However, a critical review of the literature reveals a lack of tools to evaluate environmental criteria during the initial phases of product design - specifically, the conceptual design phase and concept selection stage. The objective of this manuscript is to present a framework for the environmental evaluation of product concepts based on design for environment (DfE), with an emphasis on tangible products. The framework could enhance the effectiveness of implementing DfE strategies in the conceptual design phase. The framework was based on DfE strategies related to the physical dimension of products and the analytical hierarchy process (AHP). This is one of the very first studies to present a framework for selecting a concept that best meets DfE criteria from a product life cycle perspective and offers the potential to be expanded to multiple manufacturers.

Environmental assessment in the conceptual design phase of new product development

Many studies have shown the importance of incorporating environmental issues into the process of new product development (NPD). However, a critical review of the literature reveals a lack of tools to evaluate environmental criteria during the initial phases of product design - specifically, the conceptual design phase and concept selection stage. The objective of this manuscript is to present a framework for the environmental evaluation of product concepts based on design for environment (DfE), with an emphasis on tangible products. The framework could enhance the effectiveness of implementing DfE strategies in the conceptual design phase. The framework was based on DfE strategies related to the physical dimension of products and the analytical hierarchy process (AHP). This is one of the very first studies to present a framework for selecting a concept that best meets DfE criteria from a product life cycle perspective and offers the potential to be expanded to multiple manufacturers.

Managing Experimental-Data Shortfalls for Fair Screening at Concept Selection: Case Study To Estimate How Acid-Gas Injection Affects Asphaltene-Precipitation Behavior

Summary In a carbonate field under high pressure and high temperature (HP/HT), a gas-injection scheme has been assessed to improve oil recovery through pressure maintenance and miscible displacement. The potential study assumed sequential application of several gas-injection concepts, including raw-gas injection (RGI) and acid-gas injection (AGI). Flow-simulation studies of these concepts revealed a variety of compositional changes to the in-situ fluid, depending on the injection scheme and composition of the injected gases. Fluid compositional change is a common trigger of asphaltene instability; therefore, to ensure a robust gas-injection development, it is important to evaluate the risk of asphaltene precipitation. Because of high hydrogen sulfide (H2S) concentrations of AGI fluid under HP/HT in-situ reservoir conditions, it is difficult to take an experimental approach for evaluating gas-mixed asphaltene-flow assurance at a normal laboratory. Hence, at the concept-selection stage, this paper focuses on an alternative approach for numerical-modeling analysis of the AGI scenario, and presents the way in which AGI impacts asphaltene-precipitation behavior. On the basis of the asphaltene model established by applying a cubic-plus-association (CPA) equation of state (EOS), which was calibrated with the experimentally measured asphaltene-onset pressure (AOP), a new binary-interaction-parameter (BIP) correlation between H2S and hydrocarbons was incorporated to evaluate variation of the asphaltene-precipitation envelope (APE) with periodic compositional change observed from the AGI-flow simulation. In the AGI scenario, injection gas was assumed to be 90 mol% H2S and 10 mol% carbon dioxide (CO2). The original reservoir fluid contains 15 mol% H2S concentration. During the 3D reservoir-simulation study for the AGI scenario, H2S concentration in produced fluid was observed to increase up to 76 mol% at a well located near acid-gas injectors. In the APE sensitivity analysis that was conducted independently for each composition of H2S and CO2, the asphaltene model revealed that the base APE decreased as the H2S concentration increased and expanded as the CO2 concentration increased. As a result, for the mixed compositions, the opposing effects on the APE offset each other, and the acid-gas addition produced a subsequent decrease of the APE. In summary, this work supported a relative merit of AGI from a thermodynamic asphaltene-flow-assurance point of view, while verification is needed with experimental data in the next defined/detailed engineering stages. From stage-gate process-engineering points of view, this case study is also worthy to appropriately estimate potential concepts that have complexity of technical evaluation. Such complexity might be encountered when assuming an emerging condition or when introducing emerging technologies. In such cases, potential concepts are often difficult to evaluate fairly with existing technologies, but can possibly be evaluated with newly introduced or developing evaluation measures. However, these new and developing measures require cost that can be justified at the matured stage of development, but that cannot be justified at the concept-screening stage. In the future, the exploration and production (E&P) industry will be required to access more emerging fields of lesser easy oil; thus, this case study will be an example engaging a similar situation.

Prevention of Hydrate Formation in Wells Injecting CO2 into the Saline Aquifer

Summary For carbon dioxide (CO2) storage operations, hydrate formation in the CO2-injection system could be an issue for pipeline material integrity and safety. Numerous and extensive CO2-hydrate research has been carried out for pure CO2 and its mixtures, in both theoretical models and experimental determination. This study aims to use existing CO2-hydrate studies in the concept selection stage by application of sensitivity studies of representative operational conditions with laboratory-data-derived hydrate curves and commercial thermodynamic simulators. Hydrate formation was studied for various operational conditions of CO2-injection wells injecting into a saline-aquifer formation. Thermal/hydraulic analysis identified the operational conditions of the injection well. Then, thermodynamic calculation determined the hydrate tendency and minimum water content required to form hydrates. In cases in which hydrates are expected to form, it is recommended to inject thermodynamic inhibitors to prevent hydrate formation. Both monoethylene glycol (MEG) and methanol can be used readily for this application. However, when water-flushing operations are required to remove potential near-wellbore halite precipitation, MEG is preferred because it is less prone to exacerbate salting-out effects compared with methanol.

Concept Selection for Hydrocarbon Field Development Planning

Two methodologies to rank exploitation scenarios for hydrocarbon fields during screening and concept selection stages are described and compared. First a selection based on net present value valuation is introduced and an explanation on its limitations for field planning are given thus, a second selection based on a multiattribute decision model where other technical factors not directly associated to economics such as operability and reliability are considered. A comparison of net present value and the multiattribute decision model on a concept selection study case shows differences on the scenario selection for exploitation. Sources of the different outcomes between the two methodologies are identified. A stochastic analysis for the multiattribute decision model is performed to have a complete view of the possible outcomes since the factors in the multiattribute decision model are measured qualitatively and their values can vary depending on experts’ knowledge and experience. Recommendations obtained from the methodologies studied for screening and concept selection are given.

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.

Mapping Queries to the Linking Open Data Cloud: A Case Study Using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations, we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.

Development Optimization Using Reservoir Response Surfaces: Methods for Integrating Facility and Operational Options

The objective of a field development optimization process, or workflow, is to investigate various options and determine a course of action that will deliver the largest expected value from an asset. The analysis is often complicated by uncertainty in important inputs. Ideally, operators desire workflows and tools that integrate reservoir engineering and optimization principles in a fast-solving model that can be used to explore the full range of the uncertain inputs. This need is acute in the screening and concept selection stage where the primary objective is to determine the sensitivity of competing concepts to the sources of uncertainty. In these early stages, model results can be used to determine whether additional information should be collected, and to narrow down the number of competing options. The objective of this research is the development of a workflow and tool that integrates reservoir response surfaces within a project optimization model that contains facility and operational options, and to use this model to investigate the impacts of uncertainty on decision making. The integration of technical options is critical because a static view of capital investment and facility constraints causes a systematic undervaluation and can introduce error to development decisions. The new workflow and integrated reservoir-economic optimization tool developed in this research leverage methods and engineering work products that are already known to industry, for example, experimental design (ED) and response surface methods (RSMs). A demonstration is provided for a gas flood project using a stylized reservoir. Specifically, we investigate the selection of initial well configurations and injection capacities while simultaneously accounting for the options to update these decisions after production information is acquired in the early periods of production. The workflow is used to optimize the development of a gas flood. As a second step, the workflow is used to solve a value of information problem.

Gas Storage Facility Design Under Uncertainty

Summary In the screening and concept-selection stages of gas storage projects, many estimates are required to value competing projects and development concepts. These estimates are important because they influence which projects are selected and which concept proceeds into detailed engineering. In most cases, there is uncertainty in all of the estimates. As a result, operators are faced with the complex problem of determining the optimal design under uncertainty. A systematic uncertainty analysis can help solve this problem and can contribute to better decisions. Ideally, the uncertainty analysis is comprehensive and includes all uncertain variables and simultaneously accounts for reservoir behavior, facility options, and economic objectives. This paper proposes and demonstrates a workflow and integrated optimization model for uncertainty analysis in gas storage. The optimization model is fast-solving and eliminates many of the constraints on the scope of the uncertainty analysis. Using this workflow or similar workflows and models should facilitate analysis and communication of results within the project team and with other stakeholders.

The Maximum Resource Allocation for Uncertainty Reduction in a Decision-analytic Concept Selection

This article addresses a procedure to identify the maximum resource allocated to uncertainty reduction activity in a decision-analytic product concept selection. Concept selection is one of the most important decisions in product development. Success of a new product depends on the goodness of a concept chosen in the concept selection stage. At the same time, it is one of the most challenging decisions, because engineers need to select a concept facing large degrees of uncertainties about future customer preferences, competitions, economic situations, feasibilities of new technologies, and product costs. Information on a particular uncertain event is sought in order to reduce uncertainty; however, information gathering is meaningful only if the benefit of additional information outweighs the cost of the effort. In decision analysis, the maximum resource allocated to an information gathering effort is defined by the expected value of perfect information (EVPI), which is calculated by assuming that the information is perfectly accurate. This article illustrates a procedure to calculate the EVPI and, thus, the maximum resource allocated to uncertainty reduction activity in a decision-analytic concept selection. The utility of the proposed approach is demonstrated in a decision-analytic concept selection for a public project.

State of the Art of Ultradeepwater Production Technologies

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper OTC 17615, "State of the Art of Ultra Deepwater Production Technologies," by J.M. Bell, SPE, Chevron; Y.D. Chin, SPE, Aker Kv ærner; and S. Hanrahan, SPE, Chevron, prepared for the 2005 Offshore Technology Conference, Houston, 2-5 May. The full-length paper summarizes some results of several recent joint industry projects (JIPs) on ultradeepwater field-development technologies. A review of emerging and existing technologies for deepwater development is presented, which would be of particular interest to technology-development personnel and asset-team personnel who are in the appraisal or concept-selection stages of a project. Introduction As offshore production moves from deep water (3,000 to 6,000 ft) to ultradeep water (6,000 to 10,000 ft) and moves farther away from infrastructures, many existing technologies reach their limits or must be improved. Several successful JIPs have demonstrated that in the high-expense and -risk environment of ultradeep water, structured collaboration aligned with business drivers is a successful formula for delivering the technologies required to meet the ultradeepwater challenge. The demand for critical technology and engineering systems to enable production from ultradeep water has triggered breakthroughs in many conventional technologies. The full-length paper focuses on sub-sea, flow-assurance, floating-production-system, and riser technology.

The design of BP ETAP platform against gas explosions

Risks of personal injury from gas explosion, together with fire and smoke ingress, were among the key hazards that the Eastern Trough Area Project (ETAP) team intended to design out as far as possible. This paper describes the process ETAP followed to achieve this. The process involved the early application of the appropriate advance technology and personnel at the concept selection stage and right through different stages during design, and an integrated team including explosion specialists. All major design decisions on explosion optimisation were made at the early stage of front-end engineering design (FEED), resulting in a relatively straightforward detailed design phase. These early design decisions had the effect of not only reducing gas explosion consequences, but simplifying layout, e.g. reducing pipe run and structures. The end result is a design which gives inherently low risk to personnel and Temporary Refuge impairment without the uncertainties of high cost of late remedial work to take account of high explosion loads, and consequent project delay.

Application of MEMS technology in automotive sensors and actuators

Sensors and actuators are the critical system components that collect and act on information in the analog environment and link it to the world of digital electronics. The functional groups of sensors, software, controller hardware, and actuators from the backbone of present and future automotive systems. Unit volumes for sensors and actuators in the automotive industry are measured in millions per year and at a unit cost of a few dollars. The design of sensors and actuators has increasingly made use of microelectromechanical systems (MEMS) technology. This technology is well suited to producing a class of micromachined sensors and actuators that combines signal processing and communications on a single silicon chip or contained within the same package. This paper contains a discussion of the issues in producing MEMS sensors and actuators from the concept selection stage to the manufacturing platform. Examples of commercial and emerging automotive sensors and actuators are given, which illustrate the various aspects of device development. Future trends in MEMS technology as applied to automotive components are also discussed.

Integrated Product Design for Marketability and Manufacturing

Using as a point of departure previous work in marketing on optimal concept selection that utilizes product attribute-based customer preference and product cost models, the authors consider the concept selection stage of a new product development process. They offer empirical support for the need to push beyond these models to more complete “customer-ready” prototypes before choosing a concept to commercialize. Although the correlations between attribute-based predictions, on the one hand, and customer acceptance and product costs, on the other hand, are significant and substantial, too much variance is left unexplained to rely on these models in isolation. This implies that more detailed design work should be performed on several concepts in parallel. The authors also provide an integrated approach for concept generation and selection that leverages the unique strengths of marketing, design, and manufacturing in developing successful products.