Practical Application of Coalbed Methane Material Balance Technique to Produce Well by Well Production Forecast in Undersaturated Queensland Surat Basin Coals

2018 ◽  
Author(s):  
Gizat Aibassov
2007 ◽  
Vol 10 (03) ◽  
pp. 312-331 ◽  
Author(s):  
Christopher R. Clarkson ◽  
R. Marc Bustin ◽  
John P. Seidle

Summary Coalbed-methane (CBM) reservoirs commonly exhibit two-phase-flow (gas plus water) characteristics; however, commercial CBM production is possible from single-phase (gas) coal reservoirs, as demonstrated by the recent development of the Horseshoe Canyon coals of western Canada. Commercial single-phase CBM production also occurs in some areas of the low-productivity Fruitland Coal, south-southwest of the high-productivity Fruitland Coal Fairway in the San Juan basin, and in other CBM-producing basins of the continental United States. Production data of single-phase coal reservoirs may be analyzed with techniques commonly applied to conventional reservoirs. Complicating application, however, is the unique nature of CBM reservoirs; coal gas-storage and -transport mechanisms differ substantially from conventional reservoirs. Single-phase CBM reservoirs may also display complex reservoir behavior such as multilayer characteristics, dual-porosity effects, and permeability anisotropy. The current work illustrates how single-well production-data-analysis (PDA) techniques, such as type curve, flowing material balance (FMB), and pressure-transient (PT) analysis, may be altered to analyze single-phase CBM wells. Examples of how reservoir inputs to the PDA techniques and subsequent calculations are modified to account for CBM-reservoir behavior are given. This paper demonstrates, by simulated and field examples, that reasonable reservoir and stimulation estimates can be obtained from PDA of CBM reservoirs only if appropriate reservoir inputs (i.e., desorption compressibility, fracture porosity) are used in the analysis. As the field examples demonstrate, type-curve, FMB, and PT analysis methods for PDA are not used in isolation for reservoir-property estimation, but rather as a starting point for single-well and multiwell reservoir simulation, which is then used to history match and forecast CBM-well production (e.g., for reserves assignment). CBM reservoirs have the potential for permeability anisotropy because of their naturally fractured nature, which may complicate PDA. To study the effects of permeability anisotropy upon production, a 2D, single-phase, numerical CBM-reservoir simulator was constructed to simulate single-well production assuming various permeability-anisotropy ratios. Only large permeability ratios (>16:1) appear to have a significant effect upon single-well production characteristics. Multilayer reservoir characteristics may also be observed with CBM reservoirs because of vertical heterogeneity, or in cases where the coals are commingled with conventional (sandstone) reservoirs. In these cases, the type-curve, FMB, and PT analysis techniques are difficult to apply with confidence. Methods and tools for analyzing multilayer CBM (plus sand) reservoirs are presented. Using simulated and field examples, it is demonstrated that unique reservoir properties may be assigned to individual layers from commingled (multilayer) production in the simple two-layer case. Introduction Commercial single-phase (gas) CBM production has been demonstrated recently in the Horseshoe Canyon coals of western Canada (Bastian et al. 2005) and previously in various basins in the US; there is currently a need for advanced PDA techniques to assist with evaluation of these reservoirs. Over the past several decades, significant advances have been made in PDA of conventional oil and gas reservoirs [select references include Fetkovich (1980), Fetkovich et al. (1987), Carter (1985), Fraim and Wattenbarger (1987), Blasingame et al. (1989, 1991), Palacio and Blasingame (1993), Fetkovich et al. (1996), Agarwal et al. (1999), and Mattar and Anderson (2003)]. These modern methods have greatly enhanced the engineers' ability to obtain quantitative information about reservoir properties and stimulation/damage from data that are gathered routinely during the producing life of a well, such as production data and, in some instances, flowing pressure information. The information that may be obtained from detailed PDA includes oil or gas in place (GIP), permeability-thickness product (kh), and skin (s), and this can be used to supplement information obtained from other sources such as PT analysis, material balance, and reservoir simulation.


2021 ◽  
Author(s):  
Wai Li ◽  
Jishan Liu ◽  
Jie Zeng ◽  
Yee-Kwong Leong ◽  
Derek Elsworth ◽  
...  

Abstract The process of extracting coalbed methane (CBM) is not only of significance for unconventional energy supply but also important in mine safety. The recent advance in fracking techniques, such as carbon dioxide (CO2) fracking, intensifies the complexity of stimulated coalbeds. This work focuses on developing a fully coupled multidomain model to describe and get insight into the process of CBM extraction, particularly from those compound-fractured coalbeds. A group of partial differential equations (PDEs) are derived to characterize gas transport from matrix to fractures and borehole. A stimulated coalbed is defined as an assembly of three interacting porous media: matrix, continuous fractures (CF) and radial primary hydraulic fracture (RF). Matrix and CF constitute a dual-porosity-dual-permeability system, while RF is simplified as an 1-D cracked medium. These media further form three distinct domains: non-stimulated reservoir domain (NSRD), stimulated reservoir domain (SRD) and RF. The effects of coal deformation, heat transfer, and non-thermal sorption are coupled into the model to reflect the multiple processes in CBM extraction. The finite element method is employed to numerically solve the PDEs. The proposed model is verified by comparing its simulation results to a set of well production data from Southern Qinshui Basin in Shanxi Province, China. Great consistency is observed, showing the satisfactory accuracy of the model for CBM extraction. After that, the difference between various stimulation patterns is presented by simulating the CBM extraction process with different stimulation patterns including (1) unstimulated coalbed; (2) double-wing fracture + NSRD; (3) multiple RFs + NSRD; (4) SRD + NSRD and (5) multiple RFs + SRD + NSRD. The results suggest that Pattern (5) (often formed by CO2 fracking) boosts the efficiency of CBM extraction because it generates a complex fracture network at various scales by both increasing the number of radial fractures and activating the micro-fractures in coal blocks. Sensitivity analysis is also performed to understand the influences of key factors on gas extraction from a stimulated coalbed with multiple domains. It is found that the distinct properties of different domains originate various evolutions, which in turn influences the CBM production. Ignoring thermal effects in CBM extraction will either overestimate or underestimate the production, which is the net effect of thermal strain and non-isothermal sorption. The proposed model provides a useful approach to accurately evaluate CBM extraction by taking the complex evolutions of coalbed properties and the interactions between different components and domains into account. The importance of multidomain and thermal effects for CBM reservoir simulation is also highlighted.


2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Qiujia Hu ◽  
Xianmin Zhang ◽  
Xiang Wang ◽  
Bin Fan ◽  
Huimin Jia

Production optimization of coalbed methane (CBM) is a complex constrained nonlinear programming problem. Finding an optimal decision is challenging since the coal seams are generally heterogeneous with widespread cleats, fractures, and matrix pores, and the stress sensitivities are extremely strong; the production of CBM wells needs to be adjusted dynamically within a reasonable range to fit the complex physical dynamics of CBM reservoirs to maximize profits on a long-term horizon. To address these challenges, this paper focuses on the step-down production strategy, which reduces the bottom hole pressure (BHP) step by step to expand the pressure drop radius, mitigate the formation damage, and improve CBM recovery. The mathematical model of CBM well production schedule optimization problem is formulated. The objective of the optimization model is to maximize the cumulative gas production and the variables are chosen as BHP declines of every step. BHP and its decline rate constraints are also considered in the model. Since the optimization problem is high dimensional, nonlinear with many local minima and maxima, covariance matrix adaptation evolution strategy (CMA-ES), a stochastic, derivative-free intelligent algorithm, is selected. By integrating a reservoir simulator with CMA-ES, the optimization problem can be solved successfully. Experiments including both normal wells and real featured wells are studied. Results show that CMA-ES can converge to the optimal solution efficiently. With the increase of the number of variables, the converge rate decreases rapidly. CMA-ES needs 3 or even more times number of function evaluations to converge to 100% of the optimum value comparing to 99%. The optimized schedule can better fit the heterogeneity and complex dynamic changes of CBM reservoir, resulting a higher production rate peak and a higher stable period production rate. The cumulative production under the optimized schedule can increase by 20% or even more. Moreover, the effect of the control frequency on the production schedule optimization problem is investigated. With the increases of control frequency, the converge rate decreases rapidly and the production performance increases slightly, and the optimization algorithm has a higher risk of falling into local optima. The findings of this study can help to better understanding the relationship between control strategy and CBM well production performance and provide an effective tool to determine the optimal production schedule for CBM wells.


2019 ◽  
Vol 9 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Marco Antonio Ruiz- Serna ◽  
Guillermo Arturo Alzate- Espinosa ◽  
Andrés Felipe Obando- Montoya ◽  
Hernán Dario Álvarez- Zapata

This paper presents the results about using a methodology that combines two artificial intelligence (AI) models to predict the oil, water and gas production in a Colombian petroleum field. By combining fuzzy logic (FL) and artificial neural networks (ANN) a novelty data mining procedure is implemented, including a data imputation strategy. The FL tool determines the most useful variables or parameters to include into each well production model. ANN and FIS (fuzzy inference systems) predictive models identification is developed after the data mining process. The FIS models are capable to predict specific behaviors, while ANN models are able to forecast an average behavior. The combined use of both tools under few iterative steps, allows to improve forecasting of well behavior until reach a specified accuracy level. The proposed data imputation procedure is the key element to correct false or to complete void positions into operation data used to identify models for a typical oil production field. At the end, two models are obtained for each well product, conforming an interesting tool given the best accurate prediction of fluid phase production.


2008 ◽  
Author(s):  
Jacques Danquigny ◽  
Marc Tison ◽  
Guenole Ouaye Makino ◽  
Emmanuel Segui ◽  
Michel Vie

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