Reservoir Fluid Identification by the Wavelet Transform

2003 ◽  
Vol 46 (6) ◽  
pp. 1241-1250 ◽  
Author(s):  
Wenzheng YUE ◽  
Guo TAO
Keyword(s):  
Wavelet Transform ◽  
Fluid Identification ◽  
Reservoir Fluid

Bringing the Best of Sampling While Drilling in Highly Deviated S-Profile Wells: Case Studies from a Brown Field, Sabah, Offshore Malaysia

2021 ◽  
Author(s):  
Adif Azral Azmi ◽  
Nur Ermayani Abu Zar ◽  
Raja Azlan Raja Ismail ◽  
Nadia Zulkifli ◽  
Nikhil Prakash Hardikar ◽  
...  
Keyword(s):  
Resource Assessment ◽  
Empirical Modeling ◽  
Fluid Identification ◽  
Well Completion ◽  
Reservoir Fluid ◽  
Unconsolidated Sand ◽  
Pump Rate ◽  
Production History ◽  
Drilling Operations ◽  
Pressure Regime

Abstract Sampling While Drilling has undergone significant changes since its advent early this decade. The continuum of applications has primarily been due to the ability to access highly deviated wellbores, to collect PVT quality and volume of formation fluids. The increased confidence is also a result of numerous applications with varied time-on-wall without ever being stuck. This paper demonstrates the contribution of this technology for reservoir fluid mapping that proved critical to update the resource assessment in a brown field through three infill wells that were a step-out to drill unpenetrated blocks and confirm their isolation from the main block of the field. As a part of the delineation plan, the objective was to confirm the current pressure regime and reservoir fluid type when drilling the S-profile appraisal wells with 75 degrees inclination. Certain sand layers were prone to sanding as evidenced from the field's long production history. Due to the proven record of this technology in such challenges, locally and globally, pipe-conveyed wireline was ruled out. During pre-job planning, there were concerns about sanding, plugging and time-on-wall and stuck tools. Empirical modeling was performed to provide realistic estimates to secure representative fluid samples. The large surface area pad was selected, due to its suitability in highly permeable yet unconsolidated formations. For the first well operation, the cleanup for confirming formation oil began with a cautious approach considering possible sanding. An insurance sample was collected after three hours. For the next target, drawing on the results of the first sampling, the pump rate was increased early in time, and a sample was collected in half the time. Similar steps were followed for the remaining two wells, where water samples were collected. Oil, water, and gas gradients were calculated. Lessons learnt and inputs from Geomechanics were used in aligning the probe face and reference to the critical drawdown pressure (CDP). A total of 4,821 feet (1,469 meters) was drilled. 58 pressures were acquired, with six formation fluid samples and five cleanup cycles for fluid identification purpose. The pad seal efficiency was 95%. The data provided useful insights into the current pressure regime and fault connectivity, enabling timely decisions for well completion. The sampling while drilling deployment was successful in the highly deviated S-profile wells and unconsolidated sand, with no nonproductive time. Because of the continuous circulation, no event of pipe sticking occurred, thereby increasing the confidence, especially in the drilling teams. The sampling while drilling operations were subsequent, due to batch drilling, with minimal time in between the jobs for turning the tools around. The technology used the latest generation sensors, algorithms, computations and was a first in Malaysia. The campaign re-instituted the clear value of information in the given environment and saving cost.

A new method for reservoir fluid identification

2006 ◽  
Vol 3 (2) ◽  
pp. 124-129 ◽  
Author(s):  
Wenzheng Yue ◽  
Guo Tao
Keyword(s):  
New Method ◽  
Fluid Identification ◽  
Reservoir Fluid

Mud-Gas Breakthrough Equinor Develops Real-Time Reservoir-Fluid Identification

2021 ◽  
Vol 73 (02) ◽  
pp. 37-39
Author(s):  
Trent Jacobs
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Oil And Gas ◽  
Partial Information ◽  
Learning Model ◽  
Identification System ◽  
Fluid Identification ◽  
Reservoir Fluid ◽  
Machine Learning Model ◽  
The Difference

For all that logging-while-drilling has provided since its wide-spread adoption in the 1980s, there is one thing on the industry’s wish list that it could never offer: an accurate way to tell the difference between oil and gas. A new technology created by petrotechnicals at Equinor, however, has made this possible. The innovation could be thought of as a pseudo-log, but Equinor is describing it as a reservoir-fluid-identification system. Using an internally developed machine-learning model, it compares a database of more than 4,000 reservoir samples against the real-time analysis of the mud gas that flows up a well as it is drilled. Crunched out of the technology’s various hardware and software components is a prediction on the gas/oil ratio (GOR) that the rock being drilled through will have once it is producing. Since this happens in real time, it boils down to an alert system for when drillers are tapping into uneconomic pay zones. “This is something people have tried to do for 30 years - using partial information to predict entire oil and gas properties,” said Tao Yang. He added that “the data acquisition is rather cheap compared with all the downhole tools, and it doesn’t cost you rig time,” highlighting that the mud-gas analyzer critical to the process sits on a rig or platform without interfering with drilling operations. Yang is a reservoir technology specialist at Equinor and one of the authors of a technical paper (SPE 201323) about the new digital technology that was presented at the SPE Annual Technical Conference and Exhibition in October. He and his colleagues spent more than 3 years building the system which began in the Norwegian oil company’s Houston office as a project to improve pressure/volume/temperature (PVT) analysis in tight-oil wells in North America. It has since found a home in the company’s much larger offshore business unit in Stavanger. Offshore projects designed around certain oil-production targets can face harsh realities when they end up producing more associated gas than expected. It is the difference between drilling an underperforming well full of headaches and one that will pay out hundreds of millions of dollars over its lifetime. By introducing real-time fluid identification, Equinor is trying to enforce a new control on that risk by giving drillers the information they need to pull the bit back and start drilling a side-track deeper into the formation where the odds are better of finding higher proportions of oil or condensates. At the conference, Yang shared details about some of the first field implementations, saying that in most cases the GOR predictions made by the fluid-identification system were confirmed by traditional PVT analysis from the trial wells. Unlike other advancements made on this front, he also said the new approach is the first of its kind to combine such a large database of PVT data with a machine-learning model “that is common to any well.” That means “we do not need to know where this well is located” to make a GOR prediction, said Yang.

Reservoir fluid identification and elastic modulus determination using AVO

1996 ◽  
Vol 35 (2-3) ◽  
pp. 159-165 ◽  
Author(s):  
D. Santoso ◽  
S. Alam ◽  
L. Hendrajaya ◽  
Alfian ◽  
S. Munadi ◽  
...  
Keyword(s):  
Elastic Modulus ◽  
Fluid Identification ◽  
Reservoir Fluid

Identifying Reservoir Fluids by Wavelet Transform of Well Logs

2006 ◽  
Vol 9 (05) ◽  
pp. 574-581 ◽  
Author(s):  
Wenzheng Yue ◽  
Guo Tao ◽  
Zhengwu Liu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Data Interpretation ◽  
Well Logging ◽  
Oil Field ◽  
New Method ◽  
Well Logs ◽  
Time Frequency ◽  
Frequency Information ◽  
Reservoir Fluid

Summary The wavelet-transform (WT) method has been applied to logs to extract reservoir-fluid information. In addition to the time (depth)/frequency analysis generally performed by the wavelet method, we also have performed energy spectral analysis for time/frequency-domain signals by the WT method. We have further developed a new method to identify reservoir fluid by setting up a correlation between the energy spectra and reservoir fluid. We have processed 42 models from an oil field in China using this method and have subsequently applied these rules to interpret reservoir layers. It is found that identifications by use of this method are in very good agreement with the results of well tests. Introduction An important log-analysis application is determining reservoir-fluid properties. It is common practice to calculate the water and oil saturations of reservoir formations by use of electrical logs. With the development of well-logging technology, a number of methods have been developed for reservoir-fluid typing with well logs (Hou 2002; Geng et al. 1983; Dahlberg and Ference 1984). A recent report has also described reservoir-fluid typing by the T2 differential spectrum from nuclear-magnetic-resonance (NMR) logs (Coates et al. 2001). However, because of the interference from vugs, fractures, clay content, and mud-filtrate invasion, the reservoir-fluid information contained in well logs is often concealed. The reliability of these log interpretations is thus limited in many cases. Therefore, it is desirable to find a more reliable and consistent way of reservoir-fluid typing with well logs. In this paper, we present a new method using the WT for fluid typing with well logs. The WT technique was developed with the localization idea from Gabor's short-time Fourier analysis and has been expanded further. Wavelets provide the ability to perform local analysis (i.e., analyze a small portion of a larger signal) (Daubechies 1992).This localized analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals, where more-precise low-frequency information is wanted, and shorter intervals, where high-frequency information is needed. Wavelet analysis is capable of revealing aspects of data that other signal-analysis techniques miss: aspects such as trends, breakdown points, discontinuities in higher derivatives, and self-similarity. In well-logging-data processing, wavelet analysis has been used to identify formation boundaries, estimate reservoir parameters, and increase vertical resolution (Lu and Horne 2000; Panda et al. 1996; Jiao et al. 1999; Barchiesi and Gharbi 1999). For data interpretation, however, the identification of hydrocarbon-bearing zones by wavelet analysis is still under investigation. In this study, we have developed a technique of wavelet-energy-spectrum analysis (WESA) to identify reservoir-fluid types. We have applied this technique to field-data interpretation and have achieved very good results.

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