Using Neural Network System for Casing Collapse Occurrence and Its DepthPrediction in a Middle-Eastern Carbonate Field

Using Neural Network System for Casing Collapse Occurrence and Its Depth Prediction in a Middle-Eastern Carbonate Field Saeed Salehi; Saeed Salehi Search for other works by this author on: This Site Google Scholar Geir Hareland; Geir Hareland U. of Calgary Search for other works by this author on: This Site Google Scholar Mehdi Ganji; Mehdi Ganji Search for other works by this author on: This Site Google Scholar Keivan Khademi Dehkordi; Keivan Khademi Dehkordi Search for other works by this author on: This Site Google Scholar Mahmoud Abdullahi Mahmoud Abdullahi Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/IADC Middle East Drilling and Technology Conference, Cairo, Egypt, October 2007. Paper Number: SPE-107453-MS https://doi.org/10.2118/107453-MS Published: October 22 2007 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Salehi, Saeed, Hareland, Geir, Ganji, Mehdi, Dehkordi, Keivan Khademi, and Mahmoud Abdullahi. "Using Neural Network System for Casing Collapse Occurrence and Its Depth Prediction in a Middle-Eastern Carbonate Field." Paper presented at the SPE/IADC Middle East Drilling and Technology Conference, Cairo, Egypt, October 2007. doi: https://doi.org/10.2118/107453-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE/IADC Middle East Drilling Technology Conference and Exhibition Search Advanced Search AbstractA large carbonate oil field in Iran is suffering from severe casing collapses. 48 casing collapses have been found to be reservoir compaction and poro-elastic effects and corrosion.The application of neural networks for predicting casing collapses using complex multi-dimensional field data has been undertaken. This paper shows how a neural network (ANN) system can be trained based on the parameters affecting casing collapse to estimate the potential of collapse of wells to be drilled as well as the current wells producing in the field. The potential use of this type of analysis is large in that it can be linked as a critical risking parameter in future field development analysis. Being able to quantify the potential for collapse of a well in the future can give management the foundation for a better financial decision making on what wells and where to drill them with the potential for the larger net return on the investment. The estimated collapse and corresponding depth could also benefit in the type of casing design and completion method to be selected as well as workover designs. Interpretation of the neural network results, together with engineering judgment, allowed us to conclude that using this method is technically feasible for predicting casing collapses in this field.IntroductionThe field analyzed has been produced since the early 1950's, but the first casing collapse was not observed until after 1974. Since then the collapses have increased in numbers until today. Figure 1 gives an overview of the collapse grouped over the active failure periods. In this field 48 of totally 267 wells drilled have collapsed or more then 18 %. This is seen as a serious problem and a predictive tool for estimating this occurrence is sought. A cross section of the field along the short axis seen from NE is shown in Figure 2. Based on cross section map it shows that a fault is present which intersect the G formation members from 2 to 4. This means that there is a major plane of weakness with a low dip angle present across the entire field. The fault goes nearly to the surface at South East side where there are seen quite deformed and crushed rocks. From the casing collapse data analysis it is seen that the casing collapse occur mainly in G formation member 2 to 4. Table 1 summarizes the date of casing completion and casing collapse. From previous studies there is no single mechanism for casing collapse in this field but rather combinations of mechanism contributing to the casing collapses. Of these the most prominent one is reservoir compaction. A field that is depleted may also undergo reservoir compaction, even if the reservoir rock is relatively stiff. The reservoir compaction results in increased horizontal stresses in the crestal area of the field, while in the flank there will be horizontal unloading. This will result in slip along weak bedding planes as the formation tries to adjust to the reservoir compaction taking place. Another mechanism contributing to casing collapse is internal and external corrosion of the casing. The possible cause of corrosion in this field could be contact with saline water in the G formations due to poor cement jobs, bringing oxygen to the casing creating a corrosive environment outside the casing. The main objective of this work was to be able to predict the potential for collapse occurrences. Data from 20 wells in the field was collected and analyzed using the neural network method approach. The results from the analysis were potential for collapse and corresponding collapse depth at different locations around the field on future wells. Keywords: Artificial Intelligence, spe iadc 107453, Reservoir Characterization, Upstream Oil & Gas, NW flank, reservoir geomechanics, Reservoir Compaction, neural network system, machine learning, neural network Subjects: Reservoir Characterization, Information Management and Systems, Reservoir geomechanics, Neural networks This content is only available via PDF. 2007. SPE/IADC Middle East Drilling Technology Conference & Exhibition You can access this article if you purchase or spend a download.

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