Production Data Analysis for Commingled Multilayer Gas Reservoirs--Graphical Aides for Flow Regime Identification and History Matching

In shale gas reservoirs, the production data analysis method is widely used to invert reservoir and fracture parameter, and productivity prediction. Compared with numerical models and semianalytical models, which have high computational cost, the analytical model is mostly used in the production data analysis method to characterize the complex fracture network formed after fracturing. However, most of the current calculation models ignore the uneven support of fractures, and most of them use a single supported fracture model to describe the flow characteristics, which magnifies the role of supported fracture to a certain extent. Therefore, in this study, firstly, the fractures are divided into supported fractures and unsupported fractures. According to the near-well supported fractures and far-well unsupported fractures, the SRV zone is divided into outer SRV and inner SRV. The four areas are characterized by different seepage models, and the analytical solutions of the models are obtained by Laplace transform and inverse transform. Secondly, the material balance pseudotime is introduced to process the production data under the conditions of variable production and variable pressure. The double logarithmic curves of normalized production rate, rate integration, the derivative of the integration, and material balance pseudotime are established, and the parameters are interpreted by fitting the theoretical curve to the measured data. Then, the accuracy of the method is verified by comparison the parameter interpretation results with well test results, and the influence of parameters such as the half-length and permeability of supported and unsupported fractures on gas production is analyzed. Finally, the proposed method is applied to four field cases in southwest China. This paper mainly establishes an analytical method for parameter interpretation after hydraulic fracturing based on the production data analysis method considering the uneven support of fractures, which is of great significance for understanding the mechanism of fracturing stimulation, optimization of fracturing parameters, and gas production forecast.

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Efficient handling of fault properties using the Juxtaposition Table Method

Geological Society London Special Publications ◽

10.1144/sp496-2018-192 ◽

2020 ◽

Vol 496 (1) ◽

pp. 199-207 ◽

Cited By ~ 1

Author(s):

Tor Anders Knai ◽

Guillaume Lescoffit

Keyword(s):

History Matching ◽

Oil And Gas ◽

Production Data ◽

Gas Reservoirs ◽

Fault Rock ◽

Field Development ◽

Table Method ◽

Reservoir Models ◽

Complete Life Cycle ◽

Fault Properties

AbstractFaults are known to affect the way that fluids can flow in clastic oil and gas reservoirs. Fault barriers either stop fluids from passing across or they restrict and direct the fluid flow, creating static or dynamic reservoir compartments. Representing the effect of these barriers in reservoir models is key to establishing optimal plans for reservoir drainage, field development and production.Fault property modelling is challenging, however, as observations of faults in nature show a rapid and unpredictable variation in fault rock content and architecture. Fault representation in reservoir models will necessarily be a simplification, and it is important that the uncertainty ranges are captured in the input parameters. History matching also requires flexibility in order to handle a wide variety of data and observations.The Juxtaposition Table Method is a new technique that efficiently handles all relevant geological and production data in fault property modelling. The method provides a common interface that is easy to relate to for all petroleum technology disciplines, and allows a close cooperation between the geologist and reservoir engineer in the process of matching the reservoir model to observed production behaviour. Consequently, the method is well suited to handling fault property modelling in the complete life cycle of oil and gas fields, starting with geological predictions and incorporating knowledge of dynamic reservoir behaviour as production data become available.

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Analysis of Production Data With Elliptical Flow Regime in Tight Gas Reservoirs

10.2118/117023-ms ◽

2008 ◽

Author(s):

Dmitry Badazhkov ◽

Ovsyannikov Dmitry ◽

A. Kovalenko

Keyword(s):

Flow Regime ◽

Production Data ◽

Tight Gas ◽

Gas Reservoirs ◽

Tight Gas Reservoirs ◽

Elliptical Flow

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A new method for production data analysis of tight and shale gas reservoirs during transient linear flow period

Journal of Natural Gas Science and Engineering ◽

10.1016/j.jngse.2013.05.005 ◽

2013 ◽

Vol 14 ◽

pp. 55-65 ◽

Cited By ~ 25

Author(s):

Farhad Qanbari ◽

Christopher R. Clarkson

Keyword(s):

Data Analysis ◽

Shale Gas ◽

New Method ◽

Production Data ◽

Gas Reservoirs ◽

Linear Flow ◽

Shale Gas Reservoirs

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Advanced Production Data Analysis (PDA) Techniques Using Stochastic Single-Well History Matching in Coalbed Methane (CBM) Wells

10.2523/18103-ms ◽

2014 ◽

Cited By ~ 1

Author(s):

Alastair Ong ◽

Laurent Alessio ◽

Yassine Ben Salah ◽

Christopher Connell ◽

Saeed Majidaie ◽

...

Keyword(s):

Data Analysis ◽

Coalbed Methane ◽

History Matching ◽

Production Data ◽

Single Well ◽

Advanced Production

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Rigorous gas field production data analysis

The APPEA Journal ◽

10.1071/aj18034 ◽

2019 ◽

Vol 59 (2) ◽

pp. 780

Author(s):

Christopher Evans ◽

Antony Corrie-Keilig

Keyword(s):

Data Analysis ◽

Production Data ◽

Tight Gas ◽

Brute Force ◽

Gas Reservoirs ◽

Gas Field ◽

Sensitivity Studies ◽

Head Pressure ◽

Advanced Production ◽

Insight Into

With the advent of permanent downhole gauges and automated flowing tubing head pressure measurements, today’s engineers have a veritable plethora of production data on which to characterise gas reservoirs and estimate their ultimate recovery. As consultants, the authors see datasets that have not always been examined to their fullest potential. More often than not this is due to a singular approach to analysis, rather than application of a range of analyses. This paper discusses how combining traditional and more advanced production data analysis techniques has provided insight into fields ranging from tight gas reservoirs to conventional reservoirs under active waterdrive. Such insight is not obtained from brute force application of one size fits all techniques but understanding and using the appropriate combination of techniques that are likely to illuminate the underlying physics of the reservoir at hand. The authors have seen examples where basic data analysis has identified resource ranges outside the range estimated from sensitivity studies with detailed and sophisticated but effectively singular models.

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