Automating Well Performance Monitoring of Real Time Data
- Ahmed Saleh Al-nuaim (Aramco) | Gary M. Williamson (Saudi Aramco) | Marwan M. Labban (Saudi Aramco) | Keith Richard Holdaway (SAS Institute Inc.) | Steffen Krug
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 25-28 September, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.6.8 Well Performance Monitoring, Inflow Performance, 7.6.4 Data Mining, 4.3.4 Scale, 5.6.9 Production Forecasting, 5.6.4 Drillstem/Well Testing
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Estimating reserves and predicting production in reservoirs has long been a challenge. The importance of performing accurate analysis and interpretation of reservoir behavior using only rate and pressure data as a function of time or cumulative production is fundamental to assessing remaining reserves and for forecasting production. An interactive system was developed to assist in Decline Curve Analysis (Cartesian, Exponential, Harmonic and Hyperbolic) and Production Forecast in an Integrated Reservoir Management environment.
Different techniques are often applied in short and long term production forecasting. The associated uncertainties are of a different nature for the various time scales. The handling of these uncertainties has proven to be highly challenging for most oil and gas companies. There are tendencies to underestimate uncertainty and completely fail to account for "unforeseen?? events which can erode the forecasts.
Adopting a methodology of advanced analysis of production decline curves provides important forecasting results for future production from a single well or multiple wells across reservoirs and fields. The amount of data to be analyzed must be managed and aggregated efficiently. It is imperative to ensure the data are robust and thus have been explored via workflows based on advanced analytics to remove outliers and provide spatially viable input parameter values.
This paper introduces a refreshingly intuitive navigation of the disparate data sources and input parameters aligned with a comprehensive suite of forecasting models wrapped into a web-based solution. The user is enabled with interactive graphical output to extensive "what-if?? combinations of critical input parameters across multiple forecasting models. Whether working with oil or gas reservoirs, the user has access to a rich compendium of forecasting techniques that detail accuracy indicators and interactive graphs. The relationship, for example, between p/z and cumulative gas production for typical gas reservoirs can be studied by calculating pressure response to various modes of gas production and water encroachment. Water encroachment methods considered are Schilthuis, Hurst simplified and van Everdingen-Hurst.
Two of the most challenges in petroleum industry are predicting the production rate and estimating the reserve hydrocarbon in place. Now, with rate, pressure and time data, the engineers can predict the production rate and estimating the reserve in accurate analysis and interpretation of reservoir behavior. Decline curva analysis is needed to determine the economic viability and reserves.
Most of the existing decline curve analysis models are based on the empirical Arps' equations. These methods are shown the future for both the rate and bottomhole pressure of the reservoir.
The system enables engineers to perform Decline Curve Analysis on field and reservoir data for individual or group of wells by using different methods. It provides several functions like Best-fit forecasts are provided for Cartesian, exponential, harmonic, and hyperbolic, using linear and nonlinear regression with manual correction (both of curves and parameters), save the production forecast along with parameters in database to perform graphical comparison between multiple forecasts and Manual Input of empirical parameters (constant rate to % reserve, reserves, decline rate and decline type).
|File Size||1 MB||Number of Pages||7|