The Use of Rate-Transient-Analysis Modeling To Quantify Uncertainties in Commingled Tight Gas Production-Forecasting and Decline-Analysis Parameters in the Alberta Deep Basin

Abstract

Summary The evaluation of expected ultimate recovery (EUR) for tight gas wells has generally relied upon the Arps equation for decline-curve analysis (DCA) as a popular approach. However, it is typical in tight gas reservoirs to have limited production history that has yet to reach boundary-dominated flow because of the low permeability of such systems. Commingled production makes the situation even more complicated with multiboundary behavior. When suitable analogs are not available, rate-transient analysis (RTA) can play an important role to justify DCA assumptions for production forecasting. The Deep-basin East field has been developed with hydraulically fractured vertical wells through commingled production from multiple formations since 2002. To evaluate potential of this field, DCA type curves for various areas were established according to well performance and geological trending. Multiple-segment DCA methodology demonstrated reasonable forecasts, in which one Arps equation is used to describe the rapidly decreasing transient period in early time and another equation is used for boundary-dominated flow. However, a limitation of this approach is the uncertainty of the forecast in the absence of extended production data because the EUR can be sensitive to adjustments in some assumed DCA parameters of the second segment. In this paper, we used RTA to assess reservoir and fracture properties in multiple layers and built RTA-type well models around which uncertainty analyses were performed. The distributions of the model properties were then used in Monte Carlo analysis to forecast production and define uncertainty ranges for EUR and DCA parameters. The resulting forecasts and EUR distribution from RTA modeling generally support the DCA assumptions used for the type curves for corresponding areas of the field. The study also showed how the contribution from the various commingled layers changes with time. The proposed workflow provides a fit-for-purpose way to quantify uncertainties in tight gas production forecasting, especially for cases when production history is limited and field-level numerical simulation is not practicable.

Integrated Characterization of Hydraulically Fractured Shale-Gas Reservoirs—Production History Matching

“…This technique enables the algorithm to explore the solution space and to avoid local minima. Ma et al (2008) proposed a two-stage Markov-chain Monte Carlo (MCMC) method for quantification of permeability uncertainty in history-matching reservoir models. They start by computing the observed data mismatch for a proposed change in model parameter on the basis of a linearized approximation to flow simulation.…”

Estimating Ultimate Recoveries of Unconventional Reservoirs: Knowledge Gained from the Developments Worldwide and Egyptian Challenges

“…One of these challenges is the limitations of the analytical and semi-analytical models [23], especially for tight/shale gas systems. valuable reserves estimation [16], [33], [47], [34].…”

An Analytical Model Coupled with Orthogonal Experimental Design Is Used to Analyze the Main Controlling Factors of Multi-Layer Commingled Gas Reservoirs

“…Over the past decade, numerous scholars have extensively investigated the factors influencing interlayer interference in multi-layer commingled mining through the establishment of diverse mathematical and physical models, as well as utilizing numerical simulations, laboratory tests, and other methodologies. According to variations in research methodologies, the analysis can be categorized into three main approaches: (1) Through the establishment of a two-layer model without crossflow between layers, a physical model of multi-layer commingled production in gas wells, a rate transient analysis (RTA) and a structured RTA model of gas wells, a steady-state two-phase pipe flow model, and a radial multi-layer commingled production numerical model [24][25][26][27][28], the factors influencing interlayer interference were analyzed, and it was determined that the main factors affecting interlayer interference were interlayer pressure difference, reservoir physical properties, and interlayer span. Furthermore, the impact of each layer's properties on multi-layer commingled production was evaluated, including permeability, porosity, thickness, initial pressure, and compressibility.…”