Decline Curves from Deconvolution of Pressure and Flow-Rate Measurements for Production Optimization and Prediction (2005)

Decline Curves from Deconvolution of Pressure and Flow-Rate Measurements for Production Optimization and Prediction Fikri J. Kuchuk; Fikri J. Kuchuk Schlumberger Search for other works by this author on: This Site Google Scholar Florian Hollaender; Florian Hollaender Schlumberger Search for other works by this author on: This Site Google Scholar Ihsan Murat Gok; Ihsan Murat Gok Schlumberger Search for other works by this author on: This Site Google Scholar Mustafa Onur Mustafa Onur Istanbul Technical U. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2005. Paper Number: SPE-96002-MS https://doi.org/10.2118/96002-MS Published: October 09 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Kuchuk, Fikri J., Hollaender, Florian, Gok, Ihsan Murat, and Mustafa Onur. "Decline Curves from Deconvolution of Pressure and Flow-Rate Measurements for Production Optimization and Prediction." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2005. doi: https://doi.org/10.2118/96002-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractIn this work, we propose a methodology for monitoring well productivity index (PI), forecasting production, and estimating reserves using pressure and flow rate data. Within this context, we also provide a system identification technique by computing constant-pressure rate (deconvolved rate) behavior based on deconvolution of pressure and rate measurements. These measurements can be subsurface, surface, or a combination of both. As downhole permanent monitoring systems become more widely used, data quality, frequency, accuracy, and resolution will improve.For system identification, deconvolved rate is compared with the exponential decline of the system under depletion as a base. Any deviation from the exponential decline may indicate nonlinearities about the system. With this step's input, a geologically based well/reservoir model is constructed for parameter estimation (e.g., reservoir size, pore volume, etc.) using pressure and/or flow-rate measurements. Any available pressure-transient-test data will also be considered.The methodology proposed in this work can be integrated into data-acquisition softwares for permanent sensors, to provide a real-time monitoring/diagnosis tool for well performance and prediction. Furthermore, deconvolution results and estimated parameters can be verified continuously through a convolution of pressure and rate data, without interrupting production. It is, therefore, well suited for a single well or group of wells under extended testing, equipped with downhole gauges, and flowing through surface separation and metering systems. Wells completed with permanent downhole rate and pressure instrumentation are also ideal candidates for this type of analysis. Two field examples are presented to demonstrate the application of the methodology.IntroductionEstimation of hydrocarbon-in-place and recoverable reserves[1,2] for oil and gas reservoirs based on production history of wells has been a challenging problem since the early 1900's. These estimates are essential for determining the economic viability of field developments and for booking reserves, as required by regulatory agencies. In that time, various methods for estimating reserves and predicting performance based on historical data have been developed and published in the literature. Three main methods are currently available: basic material balance,[3,4] decline-type curve analysis,[5,6] and numerical simulation techniques.[7] These methods are often used in combination.The main motivation of this paper stems from the fact that the number of surface and/or downhole permanent pressure and flow-rate measurement systems has increased considerably over the last two decades.This is particularly true for subsea wells. These measurements are used mainly for single-well completion, artificial-lift optimization, reservoir description, and real-time safety and hardware monitoring. Permanent downhole and/or surface systems collect continuous wellbore pressure and/or rate data over a long period of time and can be monitored in real-time.Analysis of long-time flow-rate data has been traditionally done using Arp's[5] decline curves to predict future production. Fetkovich[6] combined Arp's emprical decline curves with constant-wellbore-pressure solutions and brought a much more rigorous foundation to decline-curve analysis. Fetkovich[6] has also incorporated backpressure variations into the analysis. However, the effect of wellbore-pressure variations— including backpressure—that can mask the decline characteristics of curves, has not been studied in relation to deconvolution. Keywords: drillstem/well testing, pressure data, artificial intelligence, pressure change, reservoir surveillance, pressure transient analysis, rate measurement, synthetic example, example 2, vector Subjects: Well & Reservoir Surveillance and Monitoring, Formation Evaluation & Management, Pressure transient analysis, Drillstem/well testing This content is only available via PDF. 2005. Society of Petroleum Engineers You can access this article if you purchase or spend a download.