The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk

Human reliability analysis using virtual emergency scenario via a Bayesian network model

Human reliability assessments (HRA) are typically completed by eliciting expert opinion. Data used are subjective and are prone to uncertainty and errors. This thesis outlines an HRA method using a Bayesian network (BN) model to evaluate human performance in emergency scenarios using a virtual environment (VE). VE can be used to simulate emergency situations to evaluate human performance in an environment that is controlled and safe and gives access to data that is based on an experimental method, rather than expert opinion. This method involves selecting appropriate performance shaping factors (PSFs) that are varied into different states to create credible scenarios in the VE to observe human performance. The virtual experimental technique provides a way to collect data to quantify a BN. The BN approach is suited to the assessment of human reliability due to its ability to 1) characterize dependency among different performance shaping factors (PSFs) and human errors, 2) incorporate new evidence as it becomes available, and 3) quantify the impact of different PSFs on different individuals. This paper presents an extension of the work done by Musharraf et al. (2014) by introducing PSFs that were purposively selected based on the ability to implement them in the VE, their relevance to real-life situations, and whether they could be controlled to minimize the effects of variables other than the chosen PSF. The PSFs used in this paper are complexity, stress, and uncertainty

Modeling and simulation of offshore workers' behavior

The offshore oil and gas industry functions in a team work culture in which operations depend not only on individuals’ competency, but also on team skills. Team skills are even more necessary when it comes to handling emergency conditions. Emergency conditions are dynamic in nature and personnel on board are challenged with evolving high-risk situations, time pressure, and uncertainty. One way to effectively handle emergencies is to train personnel to a competency level, both individually and as a part of a team. This would increase the chance of achieving safety in a timely manner using the available resources such as information, equipment, and people. Such training involves enhancing team members' understanding of human performance, in particular, the social and cognitive aspects of effective teamwork and good decision making. Post-accident analysis of offshore accidents shows that conventional training programs are often too generic, and that they are not designed to identify and tackle the human factors that are critical for evolving offshore emergency situations. Recognition of the importance of human factors on operator performance raises the need for training that goes beyond conventional training programs and incorporates non-technical training focusing on leadership, command, decision making, communication, and teamwork. A major difficulty to design such training is that it involves practicing emergency exercises with a potentially large number of participants, each playing the appropriate role in a given scenario. Such large-scale team exercises suffer from both organizational and educational drawbacks. The amount of human and financial resources needed for such a training exercise is difficult to organize. Furthermore, it is very hard, if not impossible, to get all team members together at the same time and location. Also, the team members may have variability in the competency levels (novice versus advanced trainees) and hence different training needs. One effective and flexible solution to this problem is to use intelligent artificial agents, or ‘virtual workers’, in a virtual environment (VE) to play different roles in the team. Virtual workers are artificially intelligent agents that can reproduce behaviors that are similar to or compatible with those of a real worker. This research proposes to develop a human behavior simulation model (HBM) that can be used to create such virtual workers in the context of offshore emergency egress. The goal of this research is to develop a human behavior model that can simulate offshore workers’ emergency response under the influence of performance influencing factors (PIFs). The first part of the work focuses on understanding human behavior during offshore emergency situations. A two level, three factor experiment was conducted in a virtual environment (VE) to investigate the relationships between the PIFs and human behavior. Influence of both internal and external PIFs were investigated. Knowledge acquisition and inference processes of individuals were also investigated in the experimental study. In the second part, a computational model was developed to capture the across-subject variability observed during the experiment. Interviews with subject matter experts (SME) were conducted at this step to ensure that the model is able to produce a realistic range of human behaviors. The final step was to validate the developed behavior model. All high-level tasks to validate the HBM were performed. Special emphasis was given on acceptability criteria testing to ensure that the integrated HBM performs adequately under different operating conditions

Pedagogical studies in virtual offshore safety training

To better prepare the offshore workforce for emergencies, operators and regulators need to use evidence-based safety training. This research aims to provide such evidence by employing an experimental program to evaluate virtual environment (VE) training as a plausible means to enhance mandatory offshore egress training. Combining VE technology with a well-designed, pedagogically informed training program, and carefully selected data-mining tools, can support the development of trainee competence in emergencies by providing artificial experience in credible situations and tracking trainee performance throughout the VE training. Evidence from this research supports the use of VE training to address pedagogical gaps in the training. Key gaps include the following: 1) conventional training is predominantly provided by fixed-time instruction, which results in crews with nominal competence, 2) the frequency of recurrency training is not informed by evidence on crews’ susceptibility to forget training, 3) crews’ learning outcomes are not measured or monitored, which results in no information to assess training transfer, and 4) due to safety constraints, muster drills lack the realism of how emergency situations unfold in real life. Lessons from pedagogical theory and data-mining methodology were used to provide empirical and modeling evidence to inform offshore and maritime domains on the application of VE training. The scope of the research involved using the VE training as a human behaviour laboratory during a longitudinal study. The context of the study was to teach the necessary egress skills to evacuate a virtual oil platform during an emergency. To address the pedagogical gaps and evaluate VE training, this thesis is comprised of four research papers. The first paper investigates the influence of the simulation-based mastery learning (SBML) pedagogical framework on the development of competence at the different learning phases, specifically the acquisition, retention, and transfer of egress skills. The second paper uses human performance data from the VE training to develop a decision tree (DT) diagnostic tool to compare the efficacy of different delivery methods for VE training. The third paper evaluates the retention and maintenance of the VE training after a period of 6 to 9-months. The fourth paper uses DT modeling to evaluate skill transfer and develop a predictive tool to analyze the efficacy of VE training on a systemic level to support future adaptive training programs. The overall contribution of this research is the use of pedagogical frameworks and data-mining tools to improve the design, delivery, and assessment, of VE training. The concept of this work is established in the context of offshore and maritime safety, however the approaches are generalizable to many virtual training applications in other domains

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

A computational model of focused attention meditation and its transfer to a sustained attention task

Although meditation and mindfulness practices are widely discussed and studied more and more in the scientific literature, there is little theory about the cognitive mechanisms that comprise it. Here we begin to develop such a theory by creating a computational cognitive model of a particular type of meditation: focused attention mediation. This model was created within Prims, a cognitive architecture similar to and based on ACT-R, which enables us to make predictions about the cognitive tasks that meditation experience may affect. We implemented a model based on an extensive literature review of how the meditation experience unfolds over time. We then subjected the Prims model to a session of the Sustained Reaction to Response Task, a task typically used to study sustained attention, a faculty that may be trained with meditation practice. Analyses revealed that the model was significantly more sensitive to detecting targets and non-targets after the meditation practice than before. These results agree with empirical findings of a longitudinal study conducted in 2010. These results suggest that our approach to modeling meditation and its effects of cognition is feasible