Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.
Pore pressure analysis can be imperative while drilling, especially in certain offshore environments where abnormal pore pressures can cause serious problems such as fluid influx, kicks, and blowouts. In order to avoid such events, the prediction in real-time, or preferably ahead of time, is required. These events can have a catastrophic impact on operations and the safety of the entire platform. Real-time wellbore logs can be used to assist in the prediction of pore pressure, however, due to the high cost of downhole data acquisition and risks associated with a tool getting stuck or lost in hole, comprehensive well logs are not always available. In the absence of a measured sonic log, a predicted acoustic log can be used for input into the pore pressure prediction avoiding the risk and cost of downhole wellbore logs, however, accuracy is extremely important. A Gradient Boosting model is trained and validated on 3 different sets of features to predict acoustic wellbore logs, followed by a physics-based model for pore pressure prediction. The physics-based model is built using Eaton, density extrapolation, and other optimization methods to ensure speed and accuracy. Lastly, the model takes the predicted acoustic data to derive the pore pressure gradient, calibrated by other drilling parameters. The Gradient Boosting model reduces any impact from the lack of data availability, significantly reduces the root mean square error (RMSE) and increases the overall accuracy. The results are then calibrated to drilling events to ensure the predictions are within the range of actual recorded event data. The result from a recent case study shows that pore pressure prediction using predicted acoustic logs correlates closely with recorded drilling events. The client successfully estimated pore pressures using predicted acoustic logs, reducing the for the need to acquire costly logging data downhole. The Gradient Boosting model provides a solution to predict acoustic logs and pore pressure that is highly accurate in real-time. The drilling event calibration method then helps to avoid physical factors that cannot be captured by the model, increasing the overall reliability of the workflow. The method allows for pore pressure analysis to be carried out accurately, regardless of the downhole logs acquired.
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