Reliability of permanent downhole systems: Minimum sample and quality index

Abstract

Permanent downhole monitoring systems are responsible for measuring pressure and temperature time series and enable uninterrupted reservoir characterization during the oil field production period, playing a key role in the oil and gas industry. Located in hostile pressure and temperature environments (i) close to the reservoir, in the case of the PDG (Permanent Downhole Gauge) sensor, and (ii) at the wellhead, in the case of the TPT (Pressure and Temperature Transducer) and PT (Pressure Transducer), its data are transmitted from the subsea environment to the Floating Production Storage and Offloading (FPSO), where the Master Control System (MCS) provides the information in engineering format. This information fulfills its function in the FPSO plant and finally is stored in an onshore data historian. Such complexity, importance, and maintenance difficulty of this system make it necessary to control and manage its reliability. Therefore, the objective of this work is to increase the availability and maximize the useful life of the downhole permanent monitoring system through the reliability calculation, using the Weibull estimate with 2 parameters, and the application of an index quality of statistical inferences. The proposed method for estimating reliability uses a database containing information from permanent downhole monitoring systems of the PDG, TPT, and PT type, from January 1st, 2008 to January 9th, 2014, and considers only the failures that occur until the arrival of the data in the MCS. From the reliability results, it can be observed that stratifications of this database could generate samples with a smaller number of observations, thus inferring reliability even with a small number of samples. The deepening of this method results in the definition of the minimum sample that allows removing reliability inferences without statistical significance and a quality index that allows classifying the reliability estimates of stratified sets of the largest sample of a database. It is worth mentioning here that both methodologies developed in this work are inserted in a well monitoring system that intends to contribute to increasing the availability of pressure and temperature data for the management of well operations.