Over 20 percent of major oil and gas (O&G) incidents reported within the European Union (EU) since 1984 have been associated with corrosion under insulation (CUI) [
Using bayesian networks (BNs) Oceaneering has developed a decision support system for effective CUI risk management. The Bayesian model can be incorporated into existing risk-based assessment (RBA) systems. A key feature of the model is the ability to predict corrosion hotspots while quantifying uncertainties. The model uses probabilities based on objective data as well as subject matter expertise, which makes analytical techniques in business accessible to a wide range of users.
With a case study we illustrate how BNs can be used to assess the risk of a fuel gas line on a live asset in the North sea. The most likely estimated remaining life (ERL) is forecasted in the range of 13 to 24 years, with a worst case of 6.7 years and best case of 40 years. By comparison, the customer CUI tracker reported an ERL of 9.7 years. BNs increase flexibility for scheduling inspection intervals, enabling more targeted inspection planning. This is a significant advancement from current RBA methodologies.
Life-cycle safety and integrity management of offshore structures is a critical activity owing to the adverse consequences of structural failure, ranging from loss of life and financial consequences to environmental pollution. Historically, integrity management of substructures such as jacket structures has been the subject of more detailed investigations than the integrity management of topside structures. For instance, more specific risk-and reliability-based methodologies exist for integrity assurance and planning the inspections of jacket structures than for topside structures. This article presents a practical methodology for risk-based inspection planning of large-scale topside structural systems under different limit states (ultimate, accidental, fatigue, and serviceability) and degradation mechanisms (e.g., corrosion and fatigue crack growth), with a view to data analytics and digitalization. The main advantage of the presented methodology is in its capability in systematically ranking the different structural elements/areas relative to one another based on their assessed level of risk of failure, i.e. a risk-based differentiation, and planning the inspections and repairs accordingly for large-scale structural systems. Such an integrated approach will result in efficient and economical management of offshore topside structural assets and can be used as a consistent and coherent basis for lifetime extension or decommissioning of offshore platforms. Integrity management of offshore structures is known to involve the analysis and management of large amounts of data and information over the lifetime of the structure. Therefore, insights are provided in the article regarding how the presented risk-based methodologies can be integrated into a digitalized and datadriven interface--a topic currently under heated investigation across the petroleum industry, facilitating the analysis and management of the involved data and information in an efficient and verifiable manner.
Content of PetroWiki is intended for personal use only and to supplement, not replace, engineering judgment. SPE disclaims any and all liability for your use of such content. A two-part theorem relating conditional probability to unconditional (prior) probability, used in value of information problems but also important to acknowledge when estimating probabilities for geologically dependent prospects.
Estimating resource and reserves crosses the disciplines between geoscientists and petroleum engineers. While the geoscientist may well have primary responsibility, the engineer must carry the resource and reserve models forward for planning and economics. Volumetric estimates of reserves are among the most common examples of Monte Carlo simulation. Consider the following typical volumetric formula to calculate the gas in place, G, in standard cubic feet. In this formula, there is one component that identifies the prospect, A, while the other factors essentially modify this component.
A pixel-based model assumes that the variable to be simulated is a realization of a continuous (Gaussian) random function. Using the spatial model, search ellipse, and control data, a pixel-based method simulates values grid node by grid node. Some of the most popular pixel-based algorithms are: turning bands, sequential Gaussian, sequential indicator, truncated Gaussian, and simulated annealing. Each method can produce a range of realizations that capture the uncertainty of an regionalized variable (RV), and so the method choice here will be based on the goals and on data types and their availability. The pixel-based method works best in the presence of facies associations that vary smoothly across the reservoir, as often is the case in deltaic or shallow marine reservoirs.
A Monte Carlo model is, in principle, just a worksheet in which some cells contain probability distributions rather than values. Thus, one can build a Monte Carlo model by converting a deterministic worksheet with the help of commercial add-in software. Practitioners, however, soon find that some of their deterministic models were constructed in a way that makes this transition difficult. Redundancy, hidden formulas, and contorted logic are common features of deterministic models that encumber the resulting Monte Carlo model. Likewise, presentation of results from probabilistic analysis might seem no different from any other engineering presentation (problem statement, summary and conclusions, key results, method, and details).
Estimating capital, one of the main ingredients for any cash flow calculation, is largely in the domain of the engineering community. Petroleum engineers are responsible for drilling costs and are often involved with other engineers in estimating costs for pipelines, facilities, and other elements of the infrastructure for the development of an oil/gas field. All practicing engineers have heard horror stories of cost and schedule overruns, and some have even been involved directly with projects that had large overruns. Why did these overruns occur, and what could have been done to encompass the actual cost in the project estimate? The upstream oil/gas industry is a risky business.
Oilfield tubulars have been traditionally designed using a deterministic working stress design (WSD) approach, which is based on multipliers called safety factors (SFs). The primary role of a safety factor is to account for uncertainties in the design variables and parameters, primarily the load effect and the strength or resistance of the structure. While based on experience, these factors give no indication of the probability of failure of a given structure, as they do not explicitly consider the randomness of the design variables and parameters. Moreover, the safety factors tend to be rather conservative, and most limits of design are established using failure criteria based on elastic theory. Reliability-based approaches are probabilistic in nature and explicitly identify all the design variables and parameters that determine the load effect and strength of the structure.
This glossary was created through discussions among the steering committee for the SPE Global Integrated Workshop Series (GIWS) on Production Forecasting. Some definitions were not contested at all, others generated fierce discussions. The contract quantity is the contractually agreed volumes and limits: predefined (annual) volume of natural gas on contract level. A factor applied to forecasts to take into account the fact that a Production System will not always operate at 100% of its capacity. Available But Not Required, that part of the IPSC that is available for production but not produced because of low off-take demand.
Probability is a mathematical concept that allows predictions to be made in the face of uncertainty. The probabilistic approach in this page defines two types of uncertainty that are associated with small-scale inherent variability, commonly is associated with relatively small (meters-length) scales. The two types of uncertainty associated with small-scale inherent variability discussed are Measurement Error and Small-scale Geologic Variability. For both types of uncertainty, it is assumed that there is an underlying population that exactly defines the system of interest. As examples of small-scale inherent variability, the proportion of ripple drift lamination (geologic variability) at any location is a fixed constant, whereas the proportion within the reservoir is those constant values integrated over the entire reservoir volume.