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Research Papers: Petroleum Engineering

Risk-Based Approach to Evaluate Casing Integrity in Upstream Wells

[+] Author and Article Information
Mohammed D. Al-Ajmi

Saudi Aramco,
Abqaiq 31311, Saudi Arabia
e-mail: Mohammed.ajmi.11@aramco.com

Dhafer Al-Shehri

Department of Petroleum Engineering,
King Fahad University of Petroleum
and Minerals,
Dhahram 31261, Saudi Arabia
e-mail: alshehrida@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering,
King Fahad University of Petroleum
and Minerals,
Dhahram 31261, Saudi Arabia
e-mail: mmahmoud@kfupm.edu.sa

Nasser M. Al-Hajri

Saudi Aramco,
Abqaiq 31311, Saudi Arabia
e-mail: nasser.hajri.22@ aramco.com

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received December 15, 2017; final manuscript received May 6, 2018; published online July 2, 2018. Assoc. Editor: Ray (Zhenhua) Rui.

J. Energy Resour. Technol 140(12), 122901 (Jul 02, 2018) (13 pages) Paper No: JERT-17-1713; doi: 10.1115/1.4040237 History: Received December 15, 2017; Revised May 06, 2018

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.

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Figures

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Fig. 1

Example of temperature profile anomalies when a cross upward flow (a) or downward flow (b) takes place

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Fig. 2

Illustration of different annuli in a well

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Fig. 3

Electromagnetic induction tool

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Fig. 4

A diagram showing different possibilities when the EMIT tool reads 50% metal loss on average. Case B is the worst case scenario showing 100% metal loss on one side and 0% on the other.

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Fig. 5

Example of EMIT report. This an EMIT example of single pipe (casing only) and double pipe (casing plus tubing completion where problematic wall thinning is not significant. TMT represents nominal tubing thickness. TNT corresponds to total nominal thickness of all available pipes. TRMT symbolizes total remaining metal thickness. AMT is actual measured thickness and DT represents reduction between total nominal thickness and measured thickness.

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Fig. 6

An illustration of the external casing corrosion concept

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Fig. 7

Leaking average metal loss data normality test for 37 sample data points (N). A straight line trend of the plotted data is the condition upon which the data set is classified as normally distributed. KS stands for Kolmogorov–Smirnov test. The high p-value (more than 0.05) shows that the hypothesis of normal distribution is accepted.

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Fig. 8

Nonleaking average metal loss data normality test

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Fig. 9

Metal loss leaking and nonleaking PDF plots

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Fig. 10

Leaking ARBR normality test

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Fig. 11

Nonleaking ARBR normality test

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Fig. 12

Leaking and nonleaking ARBR normal distribution plots

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Fig. 13

Illustration of casing failure probability derivation parameters. ARBRc is an arbitrary chosen cutoff to explain the parameters. Fhotspots is the number of leaking/failed hotspots at and above ARBRc. SFhotspots is the number of leaking/failed hotspots below ARBRc. The “SF” notion means that these hotspots survived from failure at and above ARBRc and then failed below it. SShotspots is the number of nonleaking hotspots below ARBRc. The “SS” notion means that these hotspots survived from failure at and above ARBRc and are still surviving below it.

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Fig. 14

Probability of failure function

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