Abstract
Wellbore integrity management for oil and gas wells plays a vital role throughout the typical lifespan of a well. Downhole casing leaks in oil- and gas-producing wells significantly affect their shallow water horizon, the environment, and fresh water resources. Additionally, downhole casing leaks may cause seepage of toxic gases to fresh water zones and the surface, through the casing annuli. Forecasting of such leaks and proactive measures of prevention will help eliminate their consequences and, in turn, better protect the environment. The objective of this study is to formulate an effective, robust, and accurate model for predicting the corrosion rate of metal casing string using artificial intelligence (AI) techniques. The input parameters used to train AI models include casing leaks, the percentage of metal loss, casing age, and average remaining barrier ratio (ARBR). The target parameter is the corrosion rate of the metal casing string. The dataset from which the AI models were trained was comprised of 250 data points collected from 218 wells in a giant carbonate reservoir that covered a wide range of practically reasonable values. Two AI tools were used: artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs). A prediction comparison was made between these two tools. Based on the minimum average absolute percentage error (AAPE) and the highest coefficient of determination (R2) between the measured and predicted corrosion rate values, the ANN model proposed here was determined to be best for predicting the corrosion rate. An ANN-based empirical model is also presented in this study. The proposed model is based on the associated weights and biases. After evaluating the new ANN equation using an unseen validation dataset, it was concluded that the ANN equation was able to make predictions with a significantly lower AAPE and higher R2. Use of the proposed new equation is very cost-effective in terms of reducing the number of sequential surveys and experiments conducted. The proposed equation can be utilized without an AI engine. The developed model and empirical correlation are very promising and can serve as a handy tool for corrosion engineers seeking to determine the corrosion rate without training an AI model.
Highlights
The impact of corrosion on the oil and gas industry must be viewed in terms of its effects on both capital and operational expenditures, as well as health, safety, and the environment
This study explores the comparative performances of state-of-the-art and conventional artificial intelligence (AI) techniques in the prediction of corrosion rates
This paper presents an artificial neural networks (ANNs)-based empirical model that can be used as a predictive tool when implementing wellbore integrity management strategies, leading to more sustained environmental protection
Summary
The impact of corrosion on the oil and gas industry must be viewed in terms of its effects on both capital and operational expenditures, as well as health, safety, and the environment. Corrosion-related failures can increase the risk of hydrocarbon leaks and chemical discharges to the atmosphere, subsurface formations, and underground water aquifers. Such corrosion failures and leaks can occur during the drilling, production, transportation, refining, and other phases of the oil and gas field’s operation and development, presenting a potentially serious health and safety hazard. Considine et al [3] investigated state violation records, estimating that 2.6% of the 3533 gas wells drilled between 2008 and 2011 had barrier or integrity failures. Davies et al [2] estimated that 6.3% of wells drilled between 2005 and 2013 had well barrier or integrity failures; this was consistent with the conclusions of Ingraffea et al [4], who identified the number as 6.2% for unconventional wells
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
