Real Time Pressure Transient Analysis Methods Applied to Wireline Formation Test Data

1994 ◽  
Author(s):  
Mark A. Proett
Keyword(s):  
Real Time ◽  
Test Data ◽  
Transient Analysis ◽  
Time Pressure ◽  
Pressure Transient Analysis ◽  
Pressure Transient ◽  
Analysis Methods

Delivering Pressure Transient Analysis During Drawdown on ESP Wells: Case Studies and Lessons Learned

2021 ◽  
Author(s):  
Lawrence Camilleri ◽  
Mohammed Al-Jorani ◽  
Mohammed Kamal Aal Najar ◽  
Joseph Ayoub
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Flow Rate ◽  
High Frequency ◽  
Transient Analysis ◽  
Continuous Production ◽  
Complete Analysis ◽  
Pressure Transient Analysis ◽  
Pressure Transient ◽  
Production History

Abstract While pressure transient analysis (PTA) is a proven interpretation technique, it is mostly used on buildups because drawdowns are difficult to interpret. However, the deferred production associated with buildups discourages regular application of PTA to determine skin and identify boundary conditions. Several case studies are presented covering a range of well configurations to illustrate how downhole transient liquid rate measurements with electrical submersible pump (ESP) gauges enable PTA during drawdown and therefore real-time optimization. The calculation of high-frequency transient flow rates using ESP gauge real-time data is based on the principle that the power absorbed by the pump is equal to that generated by the motor. This technique is independent of fluid specific gravity and therefore is self-calibrating with changes in water cut and phase segregation. Analytical equations ensure that the physics is always respected, thereby providing the necessary repeatability. The combination of downhole transient high-frequency flow rate and permanent pressure gauge data enables PTA using commonly available analytical techniques and software, especially because superposition time is calculated accurately. The availability of continuous production history brings significant value for PTA. It makes it possible to perform history matching and to deploy semilog analysis using an accurate set of superposition time functions. However, the application of log-log analysis techniques is usually more challenging because of imperfections in input data such as noise, oversimplified production history, time-synchronization issues, or wellbore effects. These limitations are solved by utilizing high-frequency downhole data from ESP. This is possible first as superposition time is effectively an integral function, which dampens any noise in the flow rate signal. Another important finding is that wellbore effects in subhydrostatic wells are less impactful in drawdowns than in buildups where compressibility and redistribution can mask reservoir response. Key reservoir properties, in particular mobility, can nearly always be estimated, leading to better skin factor determination even without downhole shut-in. Finally, with the constraint of production deferment eliminated, drawdowns can be monitored for extended durations to identify boundaries and to perform time-lapse interpretation more efficiently. Confirming a constant pressure boundary or a change in skin enables more effective and proactive production management. In all cases considered, a complete analysis was possible, including buildup and drawdown data comparison. With the development of downhole flow rate calculation technology, it is now possible to provide full inflow characterization in a matter of days following an ESP workover, without any additional hardware or staff mobilization to the wellsite and no deferred production. More importantly, the technique provides the necessary information to diagnose the cause of underproduction, identify stimulation candidates, and manage drawdown.

scholarly journals Real-Time Computer Analytical Application in Pressure Transient Analysis of Petroleum Reservoirs

2013 ◽  
Vol 04 (08) ◽  
pp. 1186-1192
Author(s):  
Kanya Khatri ◽  
Sadiq A. Shah ◽  
Agha F. H. Pathan ◽  
Bilal Shams ◽  
Ashfaque A. Memon ◽  
...  
Keyword(s):  
Real Time ◽  
Transient Analysis ◽  
Petroleum Reservoirs ◽  
Analytical Application ◽  
Pressure Transient Analysis ◽  
Pressure Transient

Prediction of Perforation Efficiency in Gas Well Using Machine Learning

2021 ◽  
Author(s):  
Sukotrihadiyono Tejo ◽  
Yasutra Amega ◽  
Irawan Dedy
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Transient Analysis ◽  
Empirical Relation ◽  
Skin Damage ◽  
Gas Well ◽  
Pressure Transient Analysis ◽  
Pressure Transient ◽  
Rate Test ◽  
Short Time

Abstract The efficiency of perforation is an important aspect in gas well since it affects near wellbore pressure drop related to turbulent flow. The perforation efficiency is correlated with non-Darcy skin that is able to be distinguished by pressure transient analysis of isochronal test (Swift et al., 1962), or evaluated from multi-rate flow test data plot coefficients (Jones et al., 1976), or type curve of single build up test following constant-rate production (Spivey et al., 2004). A simple single rate pressure transient analysis which is supported by parameters derived from historical multi rate test data was also proven to differentiate skin damage and non-Darcy skin (Aminian et al., 2007). Unfortunately there are trade-offs between accurateness and analysis time in these aforementioned methods. Quick analysis of perforation efficiency is often needed during well completion and workover activities, to decide whether re-perforation job is required or not. To overcome the challenges of limited time for data acquisition and evaluation, an empirical relation between actual perforation length, skin damage, and laminar-turbulence flow coefficients that are obtained from short-time multi rate test is important to predict the perforation efficiency. The empirical relation will be developed using machine learning. A simple gas reservoir model is built and then run with variations of reservoir permeability, perforation interval length, near wellbore permeability, and vertical anisotropy to generate large numbers of hypothetical multi rate test data. The data set of laminar coefficient, turbulence coefficient, absolute open flow, skin damage, and perforation length will then be trained and tested to create empirical relation using supervised regression method which will afterwards be applied to several actual field cases. This study will elaborate the development of empirical relation of perforation efficiency with the distinct parameters obtained from simple short-time multi rate test data, what other factors will influence the empirical relation, as well as become the possible condition limit of the field application of the developed empirical relation.

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