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Enhanced Interpretation of Production and Geological Data in Mature Fields, Using Data Mining Techniques


Mouret, Claude, Jean-Paul Valois, Jean Chastang, Total SA, Pau, France


Mature fields have numerous data records, which are commonly fragmentary and/or have a variable quality. To address this difficulty and to extract the most from existing data, geological and production figures are processed using data mining techniques, and inter­preted together. Spatial organization and evolution of well performance is characterized.

Well performance is influenced by geology (reservoir size and connectivity, distribution of barriers and drains, petrophysics, nature of hydrocarbons…), dynamic parameters (reservoir pressure, production drive and mechanism, reservoir depletion…), well charac­teristics (density, skin, activation…) and surface factors. Corrections for non geological fac­tors make it possible to extract well behaviors which reflect geological characteristics.

Combining reservoir dynamic behavior at well scale and available geological knowledge (such as depositional environments, paleocurrents, barriers and drains, structural pat­terns…) and putting them into a coherent synthesis (good production should reflect good reservoir conditions) brings new constraints to geological interpretation which, in turn, helps in understanding production behavior. Iterations and analysis of differences allow set­ting up a model which fits geological and reservoir data, together with production behavior.

The results are used to find bypassed oil, rank reservoir sectors and make production forecasts. The enhanced geological and reservoir interpretation, the conclusions obtained using data mining techniques, directly impact the identification of previously “forgotten” resources which are calibrated on rock data.

Convincing findings illustrate our demonstration: sand belt geometry, fault patterns, ranked sectors, infill well locating.