Pressure Transient and Production Data Analysis for Hydraulic Fracture Treatment Evaluation

2006 ◽  
Author(s):  
Christine A. Ehlig-Economides ◽  
Iskander R. Diyashev ◽  
Peter P. Valko ◽  
Kolawole Babajide Ayeni ◽  
Michael Economides
Keyword(s):  
Data Analysis ◽  
Hydraulic Fracture ◽  
Treatment Evaluation ◽  
Production Data ◽  
Fracture Treatment ◽  
Pressure Transient

Evaluating Fracturing Fluid Flowback in Marcellus Using Data Mining Technologies

2015 ◽  
Author(s):  
Qiumei Zhou ◽  
Robert Dilmore ◽  
Andrew Kleit ◽  
John Yilin Wang
Keyword(s):  
Hydraulic Fracture ◽  
Marcellus Shale ◽  
Production Data ◽  
Fracture Treatment ◽  
Water Recovery ◽  
Flowback Water ◽  
Gas Recovery ◽  
Well Completion ◽  
Geological Conditions ◽  
Fracture Fluid

Abstract Natural gas recovery from low permeability unconventional reservoirs – enabled by advanced horizontal drilling and multi-stage hydraulic fracture treatment - has become a very important energy resource in the past decade. While evaluating early gas production data in order to assess likely rate decline and ultimate gas recovery has been reported in literature, flowback water recovery has been given little consideration. Fracture fluid flowback is defined herein as aqueous phase produced within three weeks following a fracture treatment (exclusive of well shut-in time). Field data from Marcellus Shale wells in Northeastern West Virginia indicated about 2-26% of the fracture fluid is recovered during flowback. However, stimulation of gas shale is a complex engineered process, and the factors that control the volumetric flowback performance are not well understood. The objective of this paper is to use post-hoc analysis to identify correlations between fracture fluid flowback and attributes of well completion and geological setting, and to identify those factors most important in predicting flowback performances. To accomplish this objective we selected a representative subset of 187 wells for which complete data are available (from a full set of 631 wells), including well location, completion data, hydraulic fracture treatment data and production data. The wells were classified into four groups based on geological settings. For each geological group, engineering and statistical analyses were applied to study the correlation between flowback data and well completion through traditional regression methods. Important factors considered to affect flowback water recovery efficiency include number of hydraulic fracture stages, lateral length, vertical depth, proppant mass applied, proppant size, fracture fluid volume applied, treatment rate, and shut-in time. The total proppant mass, proppant size and shut-in time have relatively large influence on volumetric flowback performance. The new results enable one to estimate flowback volume in a spatial domain, based on known geological conditions and completion parameters, and lead to a better understanding of flowback behaviors in Marcellus Shale. This also helps industry manage flowback water and optimize production operations.

Production-Data Analysis—Challenges, Pitfalls, Diagnostics

2010 ◽  
Vol 13 (03) ◽  
pp. 538-552 ◽  
Author(s):  
D.. Ilk ◽  
D.M.. M. Anderson ◽  
G.W.J.. W.J. Stotts ◽  
L.. Mattar ◽  
T.A.. A. Blasingame
Keyword(s):  
Data Analysis ◽  
Transient Flow ◽  
Diagnostic Methods ◽  
Production Performance ◽  
Production Data ◽  
Finite Conductivity ◽  
Reservoir Model ◽  
Pressure Transient ◽  
Reservoir Condition ◽  
Wide Range

Summary The analysis of production data to determine reservoir characteristics, completion effectiveness, and hydrocarbons in place has become very popular in recent years. Although production analysis (PA) for reservoir characterization is approaching the popularity of pressure-transient analysis (PTA), there are few consistent diagnostic methods in practice for the analysis of production data. Many of the diagnostic methods for production-data analysis are little more than observation-based approaches—and some are essentially rules of thumb. In this work, we provide guidelines for the analysis of production data, as well as identify common pitfalls and challenges. Although PTA and production-data analyses have the same governing theory (and solutions), we must recognize that pressure transient data are acquired as part of a controlled experiment, performed as a specific event [e.g., a pressure-buildup (PBU) test]. In contrast, production data are generally considered to be surveillance/monitoring data—with little control and considerable variance occurring during the acquisition of the production data. We note that since both PA and PTA have the same governing relations, it is possible "in theory" that the same deliverables of PTA can be obtained using PA. This paper attempts to provide a state-of-the-technology review of current production-data-analysis techniques/tools—particularly tools to diagnose the reservoir model and assess the reservoir condition. The reservoir model is diagnosed mainly by examining the character exhibited by the data [that is the evidence of transient flow (e.g., quarter-slope might indicate a finite-conductivity fracture, or half-slope might indicate radial/pseudoradial flow)]. In addition, one can also assess the reservoir condition by inspecting the character of production data, which can confirm the evidence of boundary-dominated flow such that unit slope may indicate the boundary-dominated-flow regime and, therefore, in-place fluid volume can be estimated. This work also identifies the challenges and pitfalls of PA—and we try to provide guidance toward best practices and best tools. To complement this mission, we use relevant field examples to address specific issues, and we illustrate the value and function of production-data analysis for a wide range of reservoir types and properties. In this work, we propose the use of a sequence of raw and enhanced data plots for the diagnostic analysis of production data. We strongly believe that a comprehensive and systematic approach for production-data diagnosis has significant importance for the analysis and forecast of production performance.

Pressure Transient and Production Data Analysis of Horizontal Well in Unconsolidated Formation in Frade, Brazil

2011 ◽  
Author(s):  
Yan Pan ◽  
Russell T. Ewy ◽  
Don Phillip Ringe ◽  
Medhat M. Kamal ◽  
Ralph Jude Affinito ◽  
...  
Keyword(s):  
Data Analysis ◽  
Horizontal Well ◽  
Production Data ◽  
Pressure Transient

Machine learning-assisted production data analysis in liquid-rich Duvernay Formation

2021 ◽  
Vol 200 ◽  
pp. 108377
Author(s):  
Bing Kong ◽  
Zhuoheng Chen ◽  
Shengnan Chen ◽  
Tianjie Qin
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Production Data
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