Constant-Rate Decline Prediction for Heterogeneous Water-Producing Gas Wells
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Solution Overview
Problem
Existing methods fail to accurately predict the production decline of water-producing gas wells in highly heterogeneous reservoirs, particularly under constant rate production conditions, which is crucial for effective gas reservoir development.
Innovation Solution
A prediction method involving data collection, Blasingame plotting, dual-medium model fitting, and Hagedom-Brown analysis to calculate reservoir heterogeneity and water invasion constants, allowing for the determination of a stable production period and subsequent decline prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional production decline analysis methods are used for water-producing gas wells in highly heterogeneous reservoirs, then the prediction model is simple, but the prediction accuracy deteriorates due to inability to account for heterogeneity and water production effects
Solution Approach 1:
The prediction method segments the production decline process into distinct phases (transient flow, pseudo-steady state, water production phase) and applies different analytical approaches to each phase. The dual-medium model segments the reservoir into matrix and fracture systems with different flow characteristics, allowing accurate prediction while maintaining manageable model complexity through phased analysis.
Solution Approach 2:
The method introduces key parameters including heterogeneity coefficient (D), water-drive constants (a, b), and phase transition points to characterize the complex system. By changing and monitoring these parameters across different production phases, the model accurately captures heterogeneity and water production effects without requiring overly complex mathematical formulations.
2Measurement precision
If existing prediction methods are applied without considering constant rate production, then the method is easier to implement, but the prediction results deteriorate for water-producing gas wells under constant rate conditions
Solution Approach 1:
The prediction model dynamically adapts to constant rate production conditions by incorporating time-dependent water production terms and adjusting flow regime transitions. The model transitions from early-time transient flow through pseudo-steady state to late-time water production dominance, with each phase having dynamically updated parameters that reflect constant rate constraints.
Solution Approach 2:
The method uses feedback from production data (cumulative gas production Gp, cumulative water production Wp, bottom hole flowing pressure pwf) to update model parameters and predict future performance. The water-drive constants and heterogeneity coefficient are calibrated using historical constant rate production data, providing feedback-based accuracy improvement for subsequent predictions.
3Measurement precision
If reservoir heterogeneity and water production are not considered, then the analysis is simpler, but the prediction fails for highly heterogeneous water-producing gas wells
Solution Approach 1:
The dual-medium model applies local quality by assigning different flow properties to matrix and fracture zones within the reservoir. The heterogeneity coefficient D locally characterizes permeability variations, while water-drive constants a and b locally describe water influx behavior. This localized parameterization captures heterogeneity and water production effects without requiring a fully three-dimensional complex model.
Data Source
AI summary
The present disclosure relates to a prediction method for constant production decline of a water-producing gas well in a highly heterogeneous reservoir. The prediction method mainly includes: collecting related data of a target water-producing gas well, fitting to obtain a water-drive constant and a water invasion constant, fitting dynamic reserves by adopting a Blasingame plotting method, conducting fitting by adopting a dual-medium model to obtain an elastic storativity ratio and an interporosity flow coefficient, calculating a reservoir heterogeneity coefficient, obtaining a flowing bottomhole pressure at the later stage of stable production, calculating formation pressure of a new day through quantitative production of the target water-producing gas well with 1 day as an iteration stride, performing iteration until the formation pressure is less than or equal to the formation pressure at the end of stable production, and drawing a prediction curve about constant production decline of the target water-producing gas well.