Engineers need to predict the production characteristics from hydraulically fractured wells in tight gas fields. Decline curve analysis (DCA) has been widely used over many years in conventional oil and gas fields. It is often applied to tight gas, but there is uncertainty regarding the period of production data needed for accurate prediction.
In this paper decline curve analysis of simulated production data from models of hydraulically fractured wells is used to to develop improved methods for calibrating decline curve parameters from production data. The well models were constructed using data from the Khazzan field in Oman. The impact of layering, permeability and drainage area on well performance is also investigated. The contribution of each layer to recovery and the mechanisms controlling that contribution is explored.
The investigation shows that increasing the amount of production data used to fit a hyperbolic decline curve does not improve predictions of recovery unless that data comes from many years (20 years for a 1mD reservoir) of production. This is because there is a long period of transient flow in tight gas reservoirs that biases the fitting and results in incorrect predictions of late time performance. Better predictions can be made by estimating the time at which boundary dominated flow is first observed (tb), omitting the preceding transient data and fitting the decline curve to a shorter interval of data starting at tb. For single layer cases, tb can be estimated analytically using the permeability, porosity, compressibility and length scale of the drainage volume associated with the well. Alternatively, tb can be determined from the production data allowing improved prediction of performance from 2-layer reservoirs provided that a) there is high cross-flow or b) there is no cross-flow and the lower permeability layer either does not experience BDF during the field life time or it is established quickly.
Recoverable hydrocarbon resource assessments underpin decision making and business planning in the oil and gas industry. Understanding the uncertainty associated with the resource assessments are key to sound decisions that are robust against low or high outcomes. This paper outlines a probabilistic approach to resource assessment in order to characterise resource uncertainty in a portfolio containing primarily Coal Seam Gas resources.
The Probabilistic Resource Assessment (PRA) process outlined in this paper allows calculation of risked and unrisked probabilistically derived commercially recoverable resources at a field or permit level as well as at a portfolio level. This process incorporates Undiscovered ("Prospective") resources and Contingent Resources as well as resources that are producing or are under development. The key steps in this process include: definition of input distributions, probabilistic calculation of technically recoverable resources at a field level, estimation of economic chance of success, probabilistic estimate of commercially recoverable resource and aggregation of resources to a portfolio level.
This process has been applied within an integrated joint venture supplying Liquefied Natural Gas (LNG) and domestic gas markets. The process has been used primarily to understand the uncertainty range of the total resource as well as the production profile within the upstream portfolio. Sensitivities to product prices or development costs can be investigated to enable a deep understanding of the key drivers and variables of the resource assessment.
Various methods for determining recoverable hydrocarbon resources have been well documented. Broadly speaking, these methods can be categorised as probabilistic methods and deterministic methods. Typically, unconventional resources are assessed using deterministic methods. The process presented here is a robust probabilistic approach to determine a risked view of recoverable resources within an entire portfolio including both unconventional and conventional resources.