Estimation of Production Forecast Uncertainty for a Mature Production License

Jensen, Trond B.

doi:10.2118/49091-ms

Cited by 26 publications

(9 citation statements)

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“…The probability of each final model is equivalent to the product of the conditional probability of its attributes. Jensen (1998) presented another similar example of this type of application.…”

Section: Methodsmentioning

confidence: 99%

Use of representative models in the integration of risk analysis and production strategy definition

Schiozer

Ligero

Suslick

et al. 2004

Journal of Petroleum Science and Engineering

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Section: Methodsmentioning

confidence: 99%

Use of representative models in the integration of risk analysis and production strategy definition

Schiozer

Ligero

Suslick

et al. 2004

Journal of Petroleum Science and Engineering

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“…Jensen (8) concluded that the impact of each attribute must be studied in many steps throughout the production forecast, as some of them may have a stronger influence in the beginning of production, while others may be only important in latter stages. Uncertain attributes are combined, building a set of complete simulation models, which are treated by a statistical approach.…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…Uncertain attributes are combined, building a set of complete simulation models, which are treated by a statistical approach. The case studied by Jensen (8) was a North Sea field with significant history data. However, reservoir and geological parameters that could lead models with poor history matching were not used.…”

Section: Uncertainty Analysismentioning

confidence: 99%

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Integration of History Matching And Uncertainty Analysis

Schiozer

Netto

Ligero

et al. 2005

Journal of Canadian Petroleum Technology

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The most common procedure to perform a production history matching is to start with a base model and modify reservoir and fluid properties to adjust simulation results with the production history of the field. This paper presents an example of a different procedure. The history matching starts with an uncertainty analysis where several possible models are generated and the models that do not reproduce the behaviour of the reservoir are discarded:allowing a faster history matching process;increasing confidence in the process; and,adding uncertainty analysis to the production prediction. The methodology starts with a dynamic sensitivity analysis based on simulation of models where uncertain attributes are tested and compared with a base model. The attributes are then selected and combined in a derivative tree. Several models are evaluated and those that do not match the production and pressure history are discarded, reducing uncertainty in prediction of the behaviour of the reservoir. This methodology was motivated by a reservoir with unexpected behaviour that yielded a difficult history matching when considering the usual procedures. Techniques were developed to analyze reservoir performance and differential pressure maps between zones. New approaches to assess connectivity between zones were used to give alternate structural models to the sensitivity analysis. This methodology may be helpful during the first years of production when uncertainties are still significant and when typical procedures to perform production history matching are unable to solve the problem efficiently. Introduction History matching is one of the most important activities during the development and management of petroleum reservoirs. Matched models are necessary:to ensure reliable production forecasts; and,to increase the confidence in understanding the geological and reservoir models. Petroleum reservoirs are very complex structures and several uncertainties are present in the process promoting low confidence in reservoir simulation models. During the first years of production, uncertainties still play a major role in the process making it more complex and allowing multiple acceptable solutions(1). As production increases, the quantity of data permits better matching but the complexity of the process increases because of the high number of wells and objective functions of the optimization process, which usually are the difference between simulation results and observed data for pressure and each of the production phases. These different levels of uncertainty and procedures, as functions of reservoir development stages, add an extra complexity to the usual procedures(2, 3). Due to these difficulties, this paper presents a different approach. Instead of starting with a base (deterministic) model that is modified to reproduce production history, it is possible to start with an uncertainty analysis, consider several possible realizations of the reservoir model, and then to decrease the level of uncertainty in the production prediction by discarding models that do not satisfy specific conditions. There are several advantages in the proposed methodology: 1) the dynamic behaviour of the history matching is better handled with this approach since previous simulation runs can be used at any moment of the analysis;

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“…CART has been applied in several areas, such as the financial industry (Cashin and Dattagupta 2008), manufacturing and marketing (Chen and Su 2008), and medical industries (Snousy et al 2011), and even in weed science (Wiles and Brodahl 2004). Different versions of decision trees have also been applied in the petroleum industry to estimate production profiles along with uncertainty assessments in long-term production forecasts (Jensen 1998); for data classification and partitioning to predict permeability from well logs (Perez et al 2005); for case-based reasoning and planning of the execution of a fracturing job (Popa and Wood 2011); to predict average production of a well from several variables, such as producer, acid volume, and strength (Yarus et al 2006); and to predict the oil production from five significant parameters (permeability, porosity, first shut-in pressure, residual oil, and water saturation) by use of a neural-based decision-tree model (Lee and Yen 2002). Recently, the boosted regression tree was applied to the data from more than 15,000 producing wells in the Barnett shale play to predict maximum gas rates and find the relative importance of the different inputs used in the treatment (Lafollette et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

Maučec

Singh

Bhattacharya

et al. 2015

SPE Economics &Amp; Management

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The well-treatment program is an important part of the fielddevelopment plan, and certain variables, such as job-pause time (JPT) and fracture screenout, can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. Screenout occurs because of a sudden restriction of fluid flow inside the fracture and through the perforation. The objectives of this work are to investigate whether, from existing data, it is possible to find patterns in significant variables that affect the extreme values of JPT in a particular region, and what is the most critical variable causing screenout. The answers are sought by applying a classification-and-regression tree (CART) to both categorical and continuous variables in the database. The practical application of CART is presented by use of case studies containing JPT and screenout. Significant variables are found that affect the response variables, and predictor variables are ranked in the hierarchal order of their importance. Such information can be used to control predictor variables that cause high JPT or screenout.The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a datadriven, deterministic model, one cannot calculate the confidence interval of the predicted response. The confidence in results is purely because of historical values, and the accuracy of the result produced by a tree model depends on the quality of recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by use of the normalscore transform and by dividing the data set into smaller groups by use of clustering. The approach presented in this paper analyzes a data set under limited information and high uncertainty and should lead to developing methodology for generating proxy models to find future success indices (e.g., one for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision practices to save costs in field development and the optimization process.

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Estimation of Production Forecast Uncertainty for a Mature Production License

Cited by 26 publications

References 0 publications

Use of representative models in the integration of risk analysis and production strategy definition

Use of representative models in the integration of risk analysis and production strategy definition

Integration of History Matching And Uncertainty Analysis

Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

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