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Selecting Oil Wells for Hydraulic Fracturing: A Comparison between Genetic-Fuzzy and Neuro Fuzzy Systems

American Journal of Operations Research 2014年第4期4卷 202-216页

作者： Virgilio JoseMartins Ferreira Filho antonio orestes de salvo castro Production Engineering Program Federal University of Rio de Janeiro(UFRJ)Rio de JaneiroBrazil Rio de Janeiro State University(UERJ) Rio de JaneiroBrazil

Hydraulic fracturing is widely used to increase oil well production and to reduce formation damage. Reservoir studies and engineering analyses are carried out to select the wells for this kind of operation. As the reservoir parameters have some diffuse characteristics, Fuzzy Inference Systems (FIS) have been tested for these selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for hydraulic fracturing, with knowledge acquired from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, despite the genetic fuzzy system being a newer process, it obtained better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variable linguistic values.

关键词： Fuzzy Logic Petroleum Genetic Algorithms Hydraulic Fracturing

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Unsupervised Methods to Classify Real Data from Offshore Wells

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American Journal of Operations Research 2021年第5期11卷 227-241页

作者： Antônio orestes de salvo castro Mayara de Jesus Rocha Santos Fabiana Rodrigues Leta Cláudio Benevenuto C. Lima Gilson Brito Alves Lima Rio de Janeiro State University (UERJ) Rio de Janeiro Brazil department of Engineering of Fluminense Federal University (UFF) Niterói Brazil Petróleo Brasileiro S.A Rio de Janeiro Brazil

In the petroleum industry, sensor data and information are valuable. It can detect, predict and help to understand processes during oil production. Offshore wells require more attention. Once workovers, maintenance, and intervention are more costly than onshore wells. Coupling data-driven methods for well-monitoring applications, two unsupervised classification methods, one statistical and one machine learning-based, are proposed to detect anomalies in well data. The novelty is presented by applying a Control Chart using a 3 standard deviations window for the Permanent Downhole Gauge Pressure sensor (P-PDG), and a Fuzzy C-means algorithm to classify data from pressure and temperature sensors in an offshore field. The main goal in structuring a classified data set is using it to train machine learning models to monitor and manage petroleum production. Modeling applications for early fault detection systems in offshore production, based on real-time data from production sensors, require classified data sets. Then, labeling two target classes: “normal” and “fault” is a key step to be implemented in order to train the machine learning models. Therefore, this paper applies two methodologies to classify a real-time data set to create a training data set divided into “normal” and “fault” classes. Thus, it is possible to visualize the abnormal events pointed out by the methodologies and compare how sensible is each method. In addition, it is proposed a random forest application to test the per

关键词： Unsupervised Classification Fuzzy C-Means Control Chart

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