Rate of penetration prediction with uncertainty assessment: Case study of a middle-east oil field

Abstract

To increase drilling operation efficiency, attempts have been made to increase the rate of bit penetration into the formations, which is a function of controllable and uncontrollable parameters. Several mathematical models are available for penetration rate estimation. However, these models are not applicable in all circumstances. The most used model for rate of penetration is the one proposed by Bourgoyne and Young and the model has shown reliable results in different oil and gas provinces. However, the model performance significantly depends on the quantity and quality of input data. When the input data are subjected to different types of errors, some extent of uncertainty may propagate to the model. In this work, a Monte Carlo approach was followed to model the uncertainty in the predicted rates of penetrations. For this purpose, input data from an Iranian oil field were acquired and processed to check their quality. After fitting a probability distribution to each input data, the level of uncertainty in the predicted rates of penetration was quantified. Results showed that with an uncertain space of input data, a probabilistic rate of penetration was predicted. For a specific depth point, i.e. 8158.6 ft, rate of penetration range was narrowed to 1.16–1.23 ft/h with a certainty of 10 % and a mean value of 1.23 ft/h. However, for higher certainty levels a wider range of rate of penetration was obtained. Therefore, at each depth point, the certainty bands related to the predicted rate of penetration should also be stated as the model output.