Abstract: A heterogeneous classifier based on actual reservoir and well data is developed to qualitatively classify oil well producer performance. Based on the classification a new well is drilled into a producing reservoir, or fluid flows in an existing well are adjusted. The data include perforation interval(s), completion type, and how far or close the perforated zones are located relative to the free water level or gas cap. The data also include geological data, such as major geological bodies like regional faults and fractures. The features may be prioritized before classification. The classifier utilizes four different techniques to apply pattern recognition on reservoir simulation vector data to classify the wells, Three of the classification techniques are supervised learning methods: Bayesian classification, dynamic time warping and neural network. The fourth classification is an unsupervised method, clustering, to automate well grouping into similar categories.

Abstract: Estimates are formed of reservoir pressure between the wells for subsurface hydrocarbon producing reservoir. The estimation is based on field data and physical laws governing the hydrocarbon flow in porous media. Information from 3-dimensional fine geological and numerical reservoir simulation models, statistical interpolation between the wells, and static bottom-hole pressure (SBHP) surveys (measurement) at wells are used to more rapidly determine 2-dimensional isobaric reservoir pressure maps for times of interest during the reservoir simulation.

Abstract: A heterogeneous classifier based on actual reservoir and well data is developed to qualitatively classify oil well producer performance, and based on the classification drill a new well into a producing reservoir or adjust fluid flows in an existing well. The data includes perforation interval(s), completion type, and how far or close the perforated zones are located relative to the free water level or gas cap. The data also include geological data, such as major geological bodies like regional faults and fractures. The features may be prioritized before classification. The classifier utilizes four different techniques to apply pattern recognition on reservoir simulation vector data to classify the wells. Three of the classification techniques are supervised learning methods: Bayesian classification, dynamic time warping and Neural Network. The fourth classification is an unsupervised method, clustering, to automate well grouping into similar categories.

Abstract: Estimates are formed of reservoir pressure between the wells for subsurface hydrocarbon producing reservoir. The estimation is based on field data and physical laws governing the hydrocarbon flow in porous media. Information from 3-dimensional fine geological and numerical reservoir simulation models, statistical interpolation between the wells, and static bottom-hole pressure (SBHP) surveys (measurement) at wells are used to more rapidly determine 2-dimensional isobaric reservoir pressure maps for times of interest during the reservoir simulation.