Human factors are identified as the major contributor to oil and gas drilling and other operations related accidents. Offshore oil and gas operations involve complex scenarios and decision-making with potentially catastrophic consequences. The current simulation-based training modules are often criticized for their lack of objective and validated measures for human factors and non-technical skills. There is also a need to include measures for enhanced situational awareness and decision-making for the offshore drilling crew. In this study, we present holistic human-centered training framework equipped with assessment techniques to analyses situational awareness of partcipants in customized well-control operations.
The training exercise used in this work included real-time well control operation customized for drilling break and kick detection scenarios. The assessment approach consisted of eye-tracking data analysis, questionnaire analysis, checklist score analysis, and communication log analysis. After individual analysis from each technique, a new framework was developed to triangulate results from each technique to provide a comprehensive assessment. The participants included seven group of novices and one group of experts. The preliminary results indicate significant differences between the situation awareness and performance of participants. Furthermore, there were observed notable differences between the perceptual, comprehensive, and projection ability of novices and experts in routine jobs on a drilling platform. The eye-tracking data features included fixation count and fixation duration, and it was inferred that eye-tracking results can be representative of cognitive abilities of the partcipants. Furthermore, the fixation count and duration results were highly correlated with the checklist scores.
Overall, the adopted methodology in this study have potential to open new avenues for human- centered training framework and improvement in traditional assessment approach. Furthermore, it can also be helpful in understanding of cognitive responses of the offshore professionals.
The paper describes an innovative approach to performance improvement using Causal Learning (CL), a method based on the general observation that a business performance is largely the outcome of the organization, processes and procedures, ways of working, constraints and norms - the systems that the business applies to itself. These system causes are often remote from physical causes of equipment failures and as such remain hidden until revealed by appropriate analysis. The objective of CL is discovering these system causes that ultimately lead to an undesired outcome or event. CL helps us "learn" the performance system, develop insights from these discoveries and recognize the specific aspects of a system that require change to shift business performance. The Company adopted this approach to improve problem solving and root cause analysis of machinery failures. The initial decision to apply CL followed several outages of power generation systems that continued to occur after previous analyses of similar events in the past. An Enhanced Problem Solving Team (EPST) was established and trained to apply Causal Learning principles to reveal the underlying system causes of these outages. In the time since that first analysis the tools and techniques of CL have been applied to other undesired or unexpected business outcomes including HSE and project work with little or no direct technical content. CL reveals the contribution of well-intended human behaviours behind unwanted outcomes (e.g.
A study by a real-time monitoring company showed that many coiled-tubing strings are retired with a lot of life left in them. It suggested companies could lower costs by using pipe for a longer time and could benefit from multicompany studies showing how their decisions compare to the competition. A coiled-tubing selective perforating and activation system that transmits critical downhole data and measurements in real time is enabling well interventions that previously could not have been executed. This year’s papers provide examples of efficiencies that have been brought about in coiled-tubing operations. The papers demonstrate how problem-solving techniques have been applied to improve such aspects as on-site processes, fit-for-purpose equipment, and more-effective treatment placement.
Investigating the causal factors of electric line worker incidents is of high priority due to the decades-long record of incidents in the electric power industry. According to Bureau of Labor Statistics (BLS, 2018), 152 electrical line installer fatalities occurred in the U.S. in 2011 through 2016. For the individual years, the fatality numbers were 26, 27, 27, 25, 26 and 21, respectively. These rates often account for the ranking of electric line installers among the most dangerous professions in the U.S. Major contributors to electric line work incidents include electrocutions, machines, tools and vehicles (BLS, 2018). Closer inspection of these contributors reveals that their antecedents consist of attentional, strategic or knowledge factors (Reason, 1997). The study presented in this article investigates the role of sustained attention as a primary contributor to electric line worker incidents.
Little research exists concerning the safety of electric power line installers and, to the authors’ knowledge, no research is available regarding attentiveness as a causal factor of installer incidents. Specifically, the effect of sustained attention and vigilance (cognitive skills of immediate relevance to incident prevention for these workers) has not been examined. Past studies of cognitive-training regimens have evaluated both the effect on the trained task and transfer of training benefit to related but untrained cognitive tasks.
Machine learning based intelligent automation is developed by extending a prior workflow of unstructured grid reservoir simulation (
Previously, an unstructured grid reservoir simulation workflow is introduced (
The result of this work enhances the unstructured grid modeling process by automatically computing the local reservoir areas for grid coarsening and refinement with respective grid density on the multi-level hierarchical grids, it avoids user's manual interaction, which is neither efficient nor user friendly. The automated workflow improves unstructured gridding efficiency and enhances user's simulation experience.
Utilizing emerging technology breakthrough such as machine learning is important towards a successful era of the Fourth Industrial Revolution (4IR), this work of workflow automation is an example of using machine learning for enhancing problem solving in reservoir simulation.
Optimal exploitation of hydrocarbon reservoirs has always been a challenge due to uncertainties posed by subsurface heterogeneities that are often not factored into field development plans. Secondary and tertiary recovery mechanisms, such as waterflooding and enhanced oil recovery (EOR), are used to enhance the oilfield recovery beyond primary recovery. However, as the field development transitions to secondary/tertiary mechanisms, the challenges in monitoring these mechanisms further increase the uncertainty in field development. If these uncertainties are not reduced or incorporated properly, the field development may easily become uneconomic. This work presents a workflow that addresses the limitation of regular waterflood surveillance while characterizing the reservoir for optimal exploitation.
The current technologies for waterflood surveillance are limited either to local surveillance methods, such as tracers, crosswell seismic and crosswell electromagnetics (EM), or to uncalibrated global realizations, such as full-field streamline simulation, with no validation between the wells (It is to be noted that a full-field reservoir simulation calibrated with production-injection data in defined time-interval is stated as a global-surveillance method in this paper). This workflow devises integration of an effective local waterflood monitoring method, crosswell EM, and a global waterflood modeling method, streamline simulation. The process of validating the parameters of a geological model and a dynamic model with time-lapse crosswell EM data significantly reduces reservoir characterization uncertainty and helps in the preparation of a precise dynamic model.
Over the years, most leading organizations across the globe have realized that innovation is essential in the active pursuit of excellence. Which is why we continuously need to find new tools, as well as evolve current methods and breathe new life into the way we work. This paper provides a unique approach to using collective experiential knowledge to solving organizational challenges by brainstorming fresh and creative ideas, develop and accelerate their implementation, and ensure value delivery.
An approach that promotes cross functional collaboration to drive innovation in the way an idea is completely realized through employee empowerment. This approach ensures empowerment to the bright and talented employees there by increasing their engagement to the organization and its challenges. Problem solving in today's environment cannot be isolated to the management, but has to be cascaded down to the employees. Knowledge café approach promises to ensure alignment of the internal innovators to the organization's strategic challenges.
Knowledge Café is a brainstorming approach that brings together group of experienced employees on a collaborative platform to brainstorm and implement solutions to resolve identified challenges. The complete process has the following seven steps:
Identify challenges for the organization Candidate Selection Ideation Retreat - Innovation Brief, familiarize challenges, brainstorm, select Ideas and form project teams Project Charter with detailed scope, team, approach, plan, budget estimates, etc. Acceleration - sponsorship and resource allocation Implementation Realization - Idea implemented and value delivered
Identify challenges for the organization
Ideation Retreat - Innovation Brief, familiarize challenges, brainstorm, select Ideas and form project teams
Project Charter with detailed scope, team, approach, plan, budget estimates, etc.
Acceleration - sponsorship and resource allocation
Realization - Idea implemented and value delivered
This is supported by an online ideation platform, "Ideas Oasis", which helps to crowdsource ideas and turn them into reality. It helps us to manage the process from idea creation, evaluation and approval, to implementation and reward. The system also provides a reward store ‘Ibdaa Souq’, where employees can get rewarded and recognized instantaneously based on ‘Ibdaa Points’.
An important aspect of empowerment is the concept of "Innovation Reputation", which is a relative ranking based on your success an innovator within the organization. The higher your reputation ranking, the more privileged you are as an employee in the organization.
Empowering employees through knowledge cafes, ensures the use of organizations collective wisdom to be utilized for organizational improvement. Participation in the knowledge café ensures that employees are continuously developed to their fullest potential at a fast pace, honing their skillsets and competencies, increasing collaboration within group and motivating teams within the workplace. The knowledge café is a tool to utilize the tacit experiential knowledge of the employees to solving organizational challenges, what is in essence is creating a "leaning organization". Such a state constantly drives the organization to:
Enhance collaboration and motivate employees through empowerment Churn out fresh and more innovative ideas Develop and accelerate the implementation of these ideas Ensure its completion and value delivery
Enhance collaboration and motivate employees through empowerment
Churn out fresh and more innovative ideas
Develop and accelerate the implementation of these ideas
Ensure its completion and value delivery
Engineering is a technical discipline. But in practice, a great deal of engineering is non-technical. So, if we work on ill-defined problems, our first task is to define the problem and identify the objectives. Different people and different teams will derive different problem definitions, have different objectives and will favor different solutions. Getting multiple players from multiple teams to agree on the objectives and the solution is an important part of engineering.
LEADERS SEEKING TO ACHIEVE FAST RESULTS through an incident investigation may overlook key motivators or competing interests affecting behaviors that drive workers to take risk. Often, the incident investigation effort is cursory and born out of a compliance necessity rather than for the learning and prevention experience, which is akin to checking the box and moving to the next priority. Workers themselves may not be cognizant of their behaviors and choices that lead to injuries and loss. Individuals rationalizing whether to take a risk suggests that there is an understood level of adverse consequence as a possible outcome of their actions. This is a broad statement and an overgeneralization of the complexity of thought processes and actions that take place in a matter of seconds. Budgett, O'Carroll and Pfannkuch (2015) comment that "As a concept, risk can refer to a probability or to a consequence or to the product of probability and consequence" (p. Probability enters as a decision-making mediator of a potential outcome and becomes a measure of the risk frequency and severity as to whether one will act knowing the possibility and extent of harm. Forethought may not be as ever-present as one might assume because many of our decisions are reflectively and emotionally made at a subconscious level.
You have access to this full article to experience the outstanding content available to SPE members and JPT subscribers. To ensure continued access to JPT's content, please Sign In, JOIN SPE, or Subscribe to JPT LOAT is based on incremental automation of the four cognitive functions of interaction. Levels of human/system interaction are described on a nine-point scale ranging from fully manual, to levels of system support for the human, to levels of automation overseen by the human, to full automation. LOAT is a powerful tool for mapping the transition from a purely manual process, to the degree of automation that any system can achieve in the early transition phase, through a timeline to full automation. The transition levels from manual to autonomous were likened to the four cognitive functions that occur in both human interaction with machinery and automated interaction with machinery.