Bottom hole pressures are valuable source of information for reservoir surveillance and management and are the heart of reservoir engineering. Real – time pressure measurements record pressure data at 5 second interval resulting in enormous accumulation of data. The size and volume of the accumulated data limit the capability of existing analysis software to load and interpret data. This paper presents an improved methodology for data quality checking and data optimization in determining reservoir pressure depletion via Autoregressive Integrated Moving Average (ARIMA) and Decision Tree Model.
Dataset was gathered from a representative reservoir from Malay Basin. The ARIMA algorithm presented was designed for quick and efficient data quality checking. The Decision Tree Model in other hand was utilized to select maximum buildup pressure for reservoir depletion point via well status parameters. The maximum pressures were selected from buildup up data when the decision tree conditions were met. Versus classical methods, the algorithm has obtained around 90% similarity. The resulting data were then can fully optimized for reserve reporting and forecasting study i.e. analysis and numerical simulation.
The paper also reports on the advantages in the application of ARIMA – Decision Tree Algorithm in pressure surveillance revealing few key advantages namely minimize the need of well intervention and optimized workflow for reservoir engineer to view, utilize, and detect reservoir depletion data. ARIMA – Decision Tree Algorithm is targeted to be installed and integrated in field historian for better overall data analysis and visualization. Results produced from the ARIMA – Decision Tree Algorithm which consist of reservoir pressure depletion data will then improve more advance analysis such as simulation and forecasting in terms of overall speed and accuracy.
As a conclusion, this paper presents the importance and application of incorporating Big Data Analytics Algorithm in reservoir management and reporting. Future work, deliverability calculations can be incorporated in the model to identify and rectify any abnormal reservoir behavior.
The paper presents a rational methodology for the simulation of the maneuvering ability of ships, while accounting for the vessel’s maneuvering properties and the ensuing environmental and navigational conditions. In this context, the effect of different rudders and weather scenarios is demonstrated and a real ship to ship collision accident under adverse weather conditions has been simulated. The presented methodology can form the basis for a decision support tool for ship's navigation in adverse weather conditions and the assessment of ship’s maneuvering devices.
Decision trees have been used for many years in conventional oil and gas plays to help managers better understand risk and expected value for a project. Although industry has rapidly shifted over the past decade to developing unconventional resources, application of decision trees to these plays has lagged behind. When building decision trees for unconventional plays, it is often unclear to evaluators how to build the tree, namely, how to estimate the probability of meeting a given commercial threshold, as well as the production profiles and costs to use for each branch of the tree. This paper presents a workflow that can be used to build a decision tree for an unconventional play in the appraisal phase of development, given ranges of uncertainty in production profiles and drilling and completion costs. In applying this workflow, managers will better understand both the drivers of uncertainty in expected value and how they can influence it via appraisal program design and setting commercial thresholds. An example from a North American unconventional play is used as an example to illustrate the steps of the workflow.
With declining trends in production and dwindling reserves for a 35-year-old offshore field, the Samarang Redevelopment Project was initiated with a vision toward implementing integrated operations as an asset-management decision-support tool. This paper describes a case study in which four reservoir models were coupled with a production-network model, with the objectives of maximizing recovery factors, identifying operational problems, and evaluating water-production effects.
Wärtsilä’s integrated infrastructure combines the bridge systems, cloud data management, data services, decision support tools, and access to real-time information for the fleet of tankers managed by Sovcomflot, Russia’s largest shipping company. Wärtsilä plans to develop a harbor tug design to maximize ecological operational sustainability to be used a new port facility being built in the Brazilian city of São Mateus, which will have environmental demands among the most stringent in the world.
He joined Rose & Associates in 2002 after 21 years with Amoco and BP Energy. Gouveia worked in a variety of technical and managerial assignments in exploration, production and reservoir engineering, strategic and business process planning, and portfolio and risk management. Prior to BP’s acquisition of Amoco in 1999, he was director of risk management for North America. In this role he was accountable for assurance of consistent project evaluation of all major capital projects. He was the recipient of the President’s Award for his work in developing Amoco Canada’s first major fractured tight gas play and the Chairman’s Award for his work in implementing project, risk, and portfolio management processes within Amoco Canada.
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. The principal tool of risk analysis, in which input variables are assumed to be density functions. Hundreds or thousands of trials are executed, sampling from these inputs and evaluating one or more outputs. At the end of the simulation, descriptive statistics are given, and histograms and cumulative functions are exhibited for each output and a sensitivity chart prioritizes the inputs for a given output.
In the last quarter-century, financial options such as "calls" and "puts" on publicly traded stocks have become an integral part of managing stock portfolios. The seminal work on financial options was done by Black and Scholes, published in 1973, and Merton, also published in 1973. Merton and Scholes shared the 1997 Nobel Prize in economics for their work. Black, Scholes, and Merton all worked on attempting to determine the value of an option. In recent years, the concepts of valuing options have been expanded from financial options to what are called "real" options in project evaluation.
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 decision making tool that allows examination of the level and significance of workplace risk for humans, equipment, weather, operations or other conditions. Determines the probability of risk occurring, the impact the risk will have and how to mitigate the risk.
Monte Carlo simulation is a process of running a model numerous times with a random selection from the input distributions for each variable. The results of these numerous scenarios can give you a "most likely" case, along with a statistical distribution to understand the risk or uncertainty involved. Computer programs make it easy to run thousands of random samplings quickly. Monte Carlo simulation begins with a model, often built in a spreadsheet, having input distributions and output functions of the inputs. The following description is drawn largely from Murtha.