Data Mining: Caliper Prediction Based on Gamma Logging While Drilling

Abstract

During offshore drilling and completion operations of production adjustment wells, logging while drilling (LWD) is often adopted to improve the timeliness of drilling. In order to provide more reliable decision-making basis under limited data conditions, a study on data mining is needed. By collecting and analyzing 55 sets of data on wells in PL Block of Bohai Oilfield, the main controlling environment factors of gamma geophysical response were identified. And based on adjacent well interpolation prediction, a mathematical model of predicted gamma/measured gamma difference versus caliper curve was built. This model was applied to E59 well, providing a decision-making basis for its subsequent casing running operation. It is found that: (1) with the rise of potassium ion concentration and mud specific gravity, the gamma logging while drilling (GLWD) geophysical response increases gradually; potassium ions have the most significant influence on gamma, and the influence becomes greater with the increase of the caliper; (2) the caliper prediction model built based on the scatter fitting function of difference gamma and measured caliper has some engineering applicability in PL Block; (3) the in-depth mining of GLWD geophysical response data is of directive significance to field drilling and completion operations, yet various sources of data need to be utilized for comprehensive analysis. The successful application of this method provides an idea for accurately guiding the drilling and completion operations, a way for raising the utilization rate of logging data, and an important technical support for increasing reserve and production and promoting engineer-geology integration in Bohai Sea.