A network-centric approach to managing risk

2014 ◽  
Vol 54 (2) ◽  
pp. 483
Author(s):  
Bruce Hankinson
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Cognitive Biases ◽  
Stakeholder Management ◽  
Predictive Modelling ◽  
Division Of Labour ◽  
Playing Field ◽  
Business Units ◽  
Systems Research ◽  
Managing Risk

The strategies of many large organisations are underpinned by top down, hierarchical management, complex forecasting and predictive modelling, standardised processes, siloed business units, division of labour, information biases and disjointed stakeholder management. Rigid and inflexible, organisations are struggling to respond to the risks associated with unpredictable, ever changing and complex operational environments. Budget blowouts into the billions of dollars, stretched resources, increasing governance, social and political interdependencies and a complex playing field that is constantly changing as it grows and matures is what oil and gas companies in Australia today face. Proponents of Australia’s massive LNG boom are doing the hard yards and they are feeling the pressure. Unfortunately with pressure comes poor decision making. Lack of access to evidence based and up to date, real time information means decisions are often made based on intuition or unqualified, out of date information due to immature systems. Research has clearly proven that intuitive decision making results in cognitive biases. These biases results in perceptual blindness or distortion (seeing things that aren’t really there), illogical interpretation (being non-sensical) and inaccurate judgments (being just plain wrong). Without a system in place to manage risks in it’s operational space, companies will continue to make poor decisions that only increase the risks they try so hard to control. This paper proposes a new approach to better understanding organisational interdependency and risk management through adoption of a network centric approach. It explores the benefits of a network centric approach and how this it can be applied in a multi dimensional environment to not only reduce risk events and costs but enable a truly resilient and competitive business.

How Heuristics and Biases Impact Judgment and Decision Making in Well Integrity Operations

2019 ◽  
Author(s):  
Catalin Teodoriu ◽  
Saeed Salehi
Keyword(s):  
Decision Making ◽  
Human Factor ◽  
Oil And Gas ◽  
Cognitive Biases ◽  
Critical Role ◽  
Organizational Factors ◽  
Judgment And Decision Making ◽  
Well Control ◽  
Well Integrity

Abstract The contribution of the human factor in major oil and gas accident events is fully-fledged and admitted. The root cause analysis and incident investigation of these accidents reveal that many of them could have been prevented, with the perception that there was a cascade of failures in human factor elements. This is easy to comprehend, as the human factor has not been accentuated thoroughly in this industry and traditionally the focus has been on personnel knowledge and competence. A previous paper presented at OMAE 2018 had a brief overview of well integrity, and the pivotal role of cementing operations in well control. The critical role of human and organizational factors in cementing operations and well control was addressed. Furthermore, an outline of the newly implemented SEMS II regulations was also offered, with insight into adjustments that could enhance this program’s modest requirements. In this paper, the goal is to examine the key heuristics that operational people employ in well integrity procedures. Some of these cognitive biases include status-quo and confirmation biases. Several examples will be discussed to show how underlying biases can lead to improper decisions. Unfortunately, some of these biases have been embedded in companies cultures for several decades now, and are hard to change overnight. Some of these can often lead to tremendous operational costs and not necessary solving the problem. It is highly recommended that training schools consider the problems of psychological biases and start implementing case studies for improvement in judgment and decision making.

Post-Investment Reviews of Oil and Gas Projects: Methodology, Lessons Learnt, and Limitations

2021 ◽  
Author(s):  
Arthur Melet
Keyword(s):  
Decision Making ◽  
Capital Investment ◽  
Oil And Gas ◽  
Stakeholder Management ◽  
Data Availability ◽  
Capital Projects ◽  
Lessons Learnt ◽  
Operation Stage ◽  
Underlying Causes ◽  
The Impact

Abstract Objectives/scope Discuss the analytical framework created by ADNOC for the implementation of post-investment reviews (PIR) of previous capital projects, and present an overview of both the results, the lessons learnt and the limitations of such exercises, based on ADNOC's return of experience on PIRs. Without sharing confidential project information, the article will focus on providing actionable insights on ADNOC's chosen approach for PIRs, including best practices in terms of data and stakeholder management. Methods, procedures, process The overall approach can be summarized as follows: Project choice: what are the tangible criteria to be used to focus PIRs on the capital projects with the highest potential in terms of learning opportunity? Data collection: what are the minimum data requirements to conduct a PIR for an O&G project? Variance analysis: what rules need to be followed to be able to generate two economics models (initial vs updated) that can be compared? Root cause analysis: how to organize the analysis process to explain the identified variances? Results, observations, conclusions PIRs can play an important role in the continuous improvement of O&G companies’ operations at the pre-investment stage, capital investment stage, and operation stage: At the investment stage, a PIR can provide insights into the effectiveness of the decision-making and, specifically, help to identify improvement areas in the valuation (project economics), assumptions, risk management, and decision-making processes. At the execution stage, PIRs can be useful to quantify the impact of project delays and cost overruns on the overall project economics, and associate such variances with the relevant underlying causes. At the operation stage, PIRs be useful to quantify the impact of OPEX, production, and price variances (actual – forecast) on overall project economics, and associate such variances with the relevant underlying causes. Limitations of PIRs Uncertainty on what projects are likely to yield the best learning opportunities. Subjectivity: PIRs are partly subjective, as the results are largely dependent on data availability and methodological choices. Applicability of recommendations and acceptance from key stakeholders

Confirmation bias in decision making for fingerprints, DNA and eyewitness evidence cannot be explained by cognitive style or thinking dispositions.

2019 ◽  
Author(s):  
Daniel Edgcumbe
Keyword(s):  
Decision Making ◽  
Cognitive Style ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Criminal Case ◽  
Court Cases ◽  
Legal Proceedings ◽  
Thinking Dispositions ◽  
Evaluation Of Evidence ◽  
Facial Composite

Pre-existing beliefs about the background or guilt of a suspect can bias the subsequent evaluation of evidence for forensic examiners and lay people alike. This biasing effect, called the confirmation bias, has influenced legal proceedings in prominent court cases such as that of Brandon Mayfield. Today many forensic providers attempt to train their examiners against these cognitive biases. Nine hundred and forty-two participants read a fictional criminal case and received either neutral, incriminating or exonerating evidence (fingerprint, eyewitness, or DNA) before providing an initial rating of guilt. Participants then viewed ambiguous evidence (alibi, facial composite, handwriting sample or informant statement) before providing a final rating of guilt. Final guilt ratings were higher for all evidence conditions (neutral, incriminating or exonerating) following exposure to the ambiguous evidence. This provides evidence that the confirmation bias influences the evaluation of evidence.

scholarly journals Cognitive biases, environmental, patient and personal factors associated with critical care decision making: A scoping review

2021 ◽  
Author(s):  
Iris E. Beldhuis ◽  
Ramesh S. Marapin ◽  
You Yuan Jiang ◽  
Nádia F. Simões de Souza ◽  
Artemis Georgiou ◽  
...  
Keyword(s):  
Decision Making ◽  
Critical Care ◽  
Scoping Review ◽  
Cognitive Biases ◽  
Personal Factors ◽  
Factors Associated ◽  
Care Decision

Effective Improvements to Reliability Based Corrosion Management

2010 ◽  
Author(s):  
Shahani Kariyawasam ◽  
Warren Peterson
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Data Gathering ◽  
Effective Area ◽  
The Face ◽  
Integrity Management ◽  
Reliability Methods ◽  
Specific Growth Rates ◽  
Safe Condition ◽  
Effective Use

Reliability methods have being adopted by oil and gas operators for integrity management decisions. These methods explicitly account for all relevant uncertainties and are designed to provide consistent safety. Consequently, a risk or reliability based approach is a very appropriate basis for decision making in the face of uncertainties. However, as in the effective use of any powerful methodology the sensitivities of the method to assumptions and limitations of applicability need to be well understood. This paper presents how improvements were made to reliability based integrity program by understanding its limitations and sensitivities. First the inputs that have the highest impact on the results were identified. These inputs are the most appropriate areas for improvement and data gathering. It is also very important to understand how the results are to be used and for what purpose. The results of this particular inline inspection based reliability assessment are used to make better excavation and repair decisions. A defect-based and joint-based decision making process is essential for determining with sufficient confidence if each defect and joint is in a safe condition. Consequently, the improvements are focused on discriminating between the myriad of defects found during an inline inspection run. Distinct field characteristics of corrosion growth are also taken into account in these improvements. The paper presents the implementation of effective area methods for future integrity probabilistic evaluations. It also describes the benefit of applying defect-specific growth rates. Finally, case studies are presented to demonstrate the effectiveness of the changes.

Errors in Surgical Neuropathology and the Influence of Cognitive Biases: The Psychology of Intelligence Analysis

2006 ◽  
Vol 130 (5) ◽  
pp. 613-616 ◽  
Author(s):  
Roger E. McLendon
Keyword(s):  
Decision Making ◽  
Cognitive Psychology ◽  
Medical Decision Making ◽  
Cognitive Biases ◽  
Correct Diagnosis ◽  
Data Sources ◽  
Medical Decision ◽  
Intelligence Analysis ◽  
First Impression ◽  
Decision Making Processes

Abstract Context.—A significant difficulty that pathologists encounter in arriving at a correct diagnosis is related to the way information from various sources is processed and assimilated in context. Objective.—These issues are addressed by the science of cognitive psychology. Although cognitive biases are the focus of a number of studies on medical decision making, few if any focus on the visual sciences. Data Sources.—A recent publication authored by Richards Heuer, Jr, The Psychology of Intelligence Analysis, directly addresses many of the cognitive biases faced by neuropathologists and anatomic pathologists in general. These biases include visual anticipation, first impression, and established mindsets and subconsciously influence our critical decision-making processes. Conclusions.—The book points out that while biases are an inherent property of cognition, the influence of such biases can be recognized and the effects blunted.

Cognitive biases in diagnosis and decision making during anaesthesia and intensive care

BJA Education ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 420-425
Author(s):  
C.S. Webster ◽  
S. Taylor ◽  
J.M. Weller
Keyword(s):  
Decision Making ◽  
Intensive Care ◽  
Cognitive Biases
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