Forecasting of Horizontal Gas Well Production Decline in Unconventional Reservoirs using Productivity, Soft Computing and Swarm Intelligence Models

The most widely used production decline forecasting tools are numerical reservoir simulation, material balance estimates and advanced methods of production decline analysis. Besides, these existing production decline evaluation techniques for unconventional reserves estimations have underlying limitations and assumptions incorporated into their formulation which can result into under- and overestimation. This further raises the debate about which decline curve analysis (DCA) is better than the other for unconventional reservoirs predictions. Currently, data-driven artificial neural network (ANN) has emerged as a new paradigm capable of mapping complex functional relationships. In this present study, ANN technology was used to calibrate tight gas carbonate field historical declining trends which exhibit high early peak production rate and quick decline. Hereafter, making reliable predictions posed as a challenge for the complex target field and hard computing protocols. Therefore, this research applied and tested the capability of backpropagation artificial neural network (BPANN), radial basis function neural network (RBNN) and generalized regression neural network (GRNN) as DCA techniques for predicting the historical production decline trends of an ultra-low porosity and permeability tight gas reservoir. The optimum trained ANN DCA models developed were validated with another surrounding well’s dataset, producing acceptable results in agreement with the actual field data. The GRNN DCA model’s testing performance was poor, and its smoothing parameter was optimized with particle swarm optimization (PSO) algorithm which gave satisfactory results comparable to standalone BPANN and RBFNN DCA models. Furthermore, the optimum ANN DCA models’ generalization strength across the entire field dataset revealed that the developed models’ predictions were robust as compared to the data-driven Arps hyperbolic and power law exponential DCA models. This was evident from the statistical performance criteria employed which indicated that BPANN, RBFNN and PSO-GRNN DCA models are plausible better fit models for matching the target field’s historical decline performances. Also, a novel non-Darcy flow horizontal well productivity evaluation model for the target field was developed based on stress sensitivity coefficient (SSC) of permeability, tortuosity factor, Klinkenberg effect, near-wellbore turbulence effect and threshold pressure gradient (TPG) for validating the ANN DCA models predictions. The productivity model was validated with published horizontal well model with closely matched results. For inflow performances, the horizontal well model with turbulence minimizes negative effects on non-Darcy flow rates than without turbulence. Additionally, pressure drawdown influences the tight gas well productivity such that the lower the pressure drawdown, the smaller the tight gas well productivity. The operating points of the tight gas well were determined through inflow performance relation and tubing performance relation at different SSC and TPG for \( 3{\raise0.5ex\hbox{$\scriptstyle 1$} \kern-0.1em/\kern-0.15em \lower0.25ex\hbox{$\scriptstyle 2$}} \) in tubing size. In another case, synthetic unconventional simulation data with long production history were used for future forecast of 5, 24 and 70 months of production rate and cumulative production which gave pretty close results for all the DCA models, unlike the real field datasets, where the empirical rate-time models under and overestimate. In all, these ANN DCA models and the horizontal well productivity model derived will serve as new computational tools for complementing existing DCA techniques for better understanding of unconventional reservoirs’ production decline performance.