Risk-Based Approach to Evaluate Casing Integrity in Upstream Wells

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Mohammed D. Al-Ajmi ◽  
Dhafer Al-Shehri ◽  
Mohamed Mahmoud ◽  
Nasser M. Al-Hajri

Downhole casing leaks in oil and gas wells will highly impact the shallow water horizons and this will affect the environment and fresh water resources. Proactive measures and forecasting of this leak will help eliminate the consequences of downhole casing leaks and, in turn, will protect the environment. Additionally, downhole casing leaks may also cause seepage of toxic gases to the fresh water zones and to the surface through the casing annuli. In this paper, we introduced a risk-based methodology to predict the downhole casing leaks in oil and gas wells using advanced casing corrosion logs such as electromagnetic logs. Downhole casing corrosion was observed to assess the remaining well life. Electromagnetic (EM) corrosion logs are the current practice for monitoring the casing corrosion. The corrosion assessment from EM logs is insufficient because these logs cannot read in multiple casings in the well. EM tool gives average reading for the corrosion in the casing at a specific depth and it does not indicate the orientation of the corrosion. EM log does not assess the 360 deg corrosion profile in the casing and it only provides average value and this may lead to wrong decision. All of this makes EM logs uncertain tools to assess the corrosion in the downhole casing. A unified criterion to assess the corrosion in the casing and to decide workover operations or not has been identified to minimize the field challenges related to this issue. A new approach was introduced in this paper to enhance the EM logs to detect the downhole casing corrosion. Corrosion data were collected from different fields (around 500 data points) to build a probabilistic approach to assess the casing failure based on the average metal loss from the EM corrosion log. The failure model was used to set the ranges for the casing failure and the probability of casing failure for different casings. The prediction of probability of failure (PF) will act as proactive maintenance which will help prevent further or future casing leaks.

2019 ◽  
Vol 11 (3) ◽  
pp. 818 ◽  
Author(s):  
Dhafer A. Al-Shehri

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.


2018 ◽  
Author(s):  
Kenyon Gowing ◽  
◽  
Hunter Vickers ◽  
Jason A. Patton ◽  
Michael G. Davis

Author(s):  
Cody Krezinksi ◽  
Parth Panchmatia ◽  
Moneeb Genedy ◽  
Sriramya Nair ◽  
Maria Juenger ◽  
...  

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