Abstract
Circulation loss is one of the most serious and complex hindrances for normal and safe drilling operations. Detecting the layer at which the circulation loss has occurred is important for formulating technical measures related to leakage prevention and plugging and reducing the wastage because of circulation loss as much as possible. Unfortunately, because of the lack of a general method for predicting the potential location of circulation loss during drilling, most current procedures depend on the plugging test. Therefore, the aim of this study was to use an Artificial Intelligence (AI)-based method to screen and process the historical data of 240 wells and 1029 original well loss cases in a localized area of southwestern China and to perform data mining. Using comparative analysis involving the Genetic Algorithm-Back Propagation (GA-BP) neural network and random forest optimization algorithms, we proposed an efficient real-time model for predicting leakage layer locations. For this purpose, data processing and correlation analysis were first performed using existing data to improve the effects of data mining. The well history data was then divided into training and testing sets in a 3:1 ratio. The parameter values of the BP were then corrected as per the network training error, resulting in the final output of a prediction value with a globally optimal solution. The standard random forest model is a particularly capable model that can deal with high-dimensional data without feature selection. To evaluate and confirm the generated model, the model is applied to eight oil wells in a well site in southwestern China. Empirical results demonstrate that the proposed method can satisfy the requirements of actual application to drilling and plugging operations and is able to accurately predict the locations of leakage layers.
Highlights
Circulation loss is a common but complex occurrence during the drilling process
This study aims to use real-time well history data and drilling parameters to predict the locations of circulation loss layers and to propose a new and effective prediction method for use in drilling and plugging operations
According to the principle that the parameters represented by the fields can be collected and obtained at the drilling site, as much data information can be retained in the cleaning data as possible for the processing, so the data can be divided into different areas
Summary
Circulation loss is a common but complex occurrence during the drilling process. Downhole leakage considerable increases drilling cost and downtime [1,2,3], which often leads to serious accidents because leakage management is a tedious process [4]. Artificial Intelligence (AI) emerged in the 1950s, it is still a relatively new area of science that studies and develops the theory, method, technology, and application of systems that are used to simulate, extend, and expand human intelligence. This study aims to use real-time well history data and drilling parameters to predict the locations of circulation loss layers and to propose a new and effective prediction method for use in drilling and plugging operations. Using AI to precisely analyze the data related to these factors, a potential law for predicting leakage layer location may be derived
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