Data Mining Effective for Casing-Failure Prediction and Prevention
Authors: Carpenter Chris 
Published: June 2019

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19311, “Data-Mining Approaches for Casing-Failure Prediction and Prevention,” by Christine Noshi, SPE, Samuel Noynaert, SPE, and Jerome Schubert, SPE, Texas A&M University, prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission.

Recent casing failures in the Granite Wash play in the western Anadarko Basin have sparked deep concerns for operators in North Texas and Oklahoma. Hydrostatic tests made in the field show that current API standards do not assure adequate joint and bursting strength to meet deep-well requirements. This paper is part of an ongoing effort to minimize the likelihood of failure using data-mining and machine-learning algorithms.

Introduction

Casing failure has long presented a challenge to the industry. The combined effects of design, dynamic borehole conditions, metallurgy, and handling have been challenging to quantify and predict accurately. Additionally, most casing-string challenges have been handled reactively instead of proactively; the total number of failures have been underreported and overlooked.

The authors focus on the effects of poor cement as a primary factor; this translates into the absence of cement in a case study presented in the complete paper. Additional factors are the pumping of corrosive acids and poor standardized casing design that does not account for varied formations along with cyclical temperatures.

Casing with partial cementation and sheaths with voids can contribute to excessive buckling-related collapse and tensile failures. Large pressure loadings, along with significant change in temperature, contribute to significant stresses in the intercasing annuli. In fragmentally cemented casing, tensile loading can show a great discrepancy between compression and high tension, with instances of failures in both the outer and inner strings. Additionally, cement thickening by downward flow could lack uniformity and could be prone to channeling. Air entrapment might occur, establishing bridges that hinder the process. Some authors in the literature related cementing failures with hole enlargements and washouts in long cement depths. The lack of cement support in those significant intervals exposed the casing to movement during drillpipe rotation, which triggered wear and ultimate buckling.

Methodology

The data were descriptively visualized using methods such as box plots, mosaic plots, and trellis charts, while predictive techniques included artificial neural networks (ANNs) and boosted-ensemble trees. A statistical software package was used along with Python coding to implement the models and choose the most-significant factors contributing to failure. Data-preprocessing techniques were implemented. The process began with data cleaning to account for missing data, re-move the bias incurred by noise, and remove outliers. For missing values, multivariate normal imputation on the basis of all samples belonging to the same class was used. Then, several parameters from different databases were integrated. Data transformation involved standardizing the data by the subtraction of the mean value and the subsequent division by standard deviation from each feature. Categorical variables were converted to numerical values because models such as neural nets, regression, and nearest-neighbor involve only numeric inputs. The compiled data set comprised 78 wells. Caution should be taken when assessing its statistical significance.