Exploration
Adel M Salem; Said Kamel Elsayed; Mohamed Y Amer; Mohammed S Farahat
Abstract
Sustainable production of sufficient energy to power the world’s economy with a minimum environmental footprint has been one of the most significant challenges for the decades. Geothermal energy has been considered as one of the promising options to meet the world’s future energy demand. ...
Read More
Sustainable production of sufficient energy to power the world’s economy with a minimum environmental footprint has been one of the most significant challenges for the decades. Geothermal energy has been considered as one of the promising options to meet the world’s future energy demand. The cost of drilling geothermal wells is between 35% and 50% of the total investment cost for the new high-temperature geothermal plants. This “up front” cost makes the geothermal plants more expensive to build than the conventional plants, and because of this and the perceived risk, a lot of attention has been focused on reducing this cost.This paper attempts to minimize the cost of drilling deep wells such as AG-119X, in Egypt of 20060 ft. in depths; in this well, the actual cost was more than the proposed by about five million USD. The actual cost of the drilling operation has been analyzed and compared with the proposed; by observing the cost of each drilling item, it was found that the power drive tools in the bottom hole assembly such as the downhole motor with Rotary Steerable drilling system (RSS) or turbodrill hydraulic downhole motor is the most costly element of the drilling operation in 8.5 holes, which tack thirteen trips in every trip with a new bit, and it was found that the turbodrill hydraulic downhole motor was costly effected in drilling the shush section, in this, and can save around 1756999 USD; this paper is a road map for reducing the cost of drilling geothermal wells.
Yahia ElSayed Khamis; Shady Galal El-Rammah; Adel M Salem
Abstract
The rate of penetration plays a key role in maximizing drilling efficiency, so it is essential for the drilling process optimization and management. Traditional mathematical models have been used with some success to predict the rate of penetration in drilling. Due to the high complexity and non-linear ...
Read More
The rate of penetration plays a key role in maximizing drilling efficiency, so it is essential for the drilling process optimization and management. Traditional mathematical models have been used with some success to predict the rate of penetration in drilling. Due to the high complexity and non-linear behavior of drilling parameters with the rate of penetration, these mathematical models cannot accurately and comprehensively predict the rate of penetration. Machine learning (ML) seems to be an attractive alternative to model this complicated physical process. This research paper introduces new data-driven models used to predict ROP using different parameters such as (depth, weight on bit (WOB), revolution per minute (RPM), Torque (T), standpipe pressure (SPP), flow in pump (pumping flow rate(Q), mud weight, hours on bit (HOB), revolutions on bit, bit diameter, total flow area (TFA), pore pressure, overburden pressure, and pit volume). Data-driven models are built using two different machine learning techniques, using 1771 raw real field data. The coding is built using the python programming language. The k-nearest neighbors (KNN) model predicting ROP for the training dataset show a correlation coefficient (R2) of 0.94. The multi-layer perceptron (MLP) model predicting ROP for the training dataset show a correlation coefficient (R2) of 0.98. We can conclude that MLP has a better accuracy, and removing outliers enhances model performance.
