Petroleum Engineering – Drilling
Hossein Yavari; Mohammad Sabah; Rassoul Khosravanian; David. A Wood
Abstract
The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including ...
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The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including mathematical models and artificial intelligence have been proposed to predict the rate of penetration. Previous research has showed that artificial intelligence such as neural network and adaptive neuro-fuzzy inference system are superior to conventional methods in the prediction of drilling rate. On the other hand, many complicated analytical ROP models have also been developed during recent years that are able to predict drilling rate with a high degree of accuracy. Therefore, comparing different approaches to find the most accurate model and assess the conditions in which each model works well can be highly effective in reducing drilling time as well as drilling cost. In this study, Hareland-Rampersad (HR) model, Bourgoyne and Young (BY) model, and an adaptive-neuro-fuzzy inference system (ANFIS) are employed to predict the drilling rate in the South Pars gas field (SP) offshore of Iran, and their results are compared to find the best ROP-prediction model for each formation. A database covering the drilling parameters, sonic log data, and modular dynamic test data collected from several drilling sites in SP are used to construct the mentioned models for each formation. The results show that when a large amount of data is available, the ANFIS is more accurate than the other approaches in predicting drilling rate. In the case of ROP models, BY model works considerably better than HR model for the majority of the formations. However, in formations where some drilling parameters are constant, but formation strength is variable, HR model shows better prediction performance than BY model.
Fatemeh Deregeh; Milad Karimian; Hossein Nezmabadi-Pour
Abstract
The late detection of the kick (the entrance of underground fluids into oil wells) leads to oil well blowouts. It causes human life loss and imposes a great deal of expenses on the petroleum industry. This paper presents the application of adaptive neuro-fuzzy inference system designed for an earlier ...
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The late detection of the kick (the entrance of underground fluids into oil wells) leads to oil well blowouts. It causes human life loss and imposes a great deal of expenses on the petroleum industry. This paper presents the application of adaptive neuro-fuzzy inference system designed for an earlier kick detection using measurable drilling parameters. In order to generate the initial fuzzy inference system, subtractive clustering is utilized. The training set contains 50 data samples and there are 362 data samples for testing the proposed method. Also, ANFIS structure is examined at different radii (the parameter of subtractive clustering). Different conformations are tested to get the earliest detection and the lowest false alarms while facing kick. Eventually, ANFIS verifies the danger exposure depth of about 28.6 meters before the depth that the kick was sensed by crew. Such an assessment gives the rig crew enough time to prepare for the danger and stop the operation before being exposed to high pressure zones.
