Automated Pressure Transient Analysis: A Cloud-Based Approach
- Yonggui Guo (Advantek Waste Management Services LLC) | Ali Zidane (Advantek Waste Management Services LLC) | Yashesh Panchal (Advantek Waste Management Services LLC) | Omar Abou-Sayed (Advantek Waste Management Services LLC) | Ahmed Abou-Sayed (Advantek Waste Management Services LLC)
- Document ID
- International Petroleum Technology Conference
- International Petroleum Technology Conference, 26-28 March, Beijing, China
- Publication Date
- Document Type
- Conference Paper
- 2019. International Petroleum Technology Conference
- 1.8 Formation Damage, 7.6.6 Artificial Intelligence, 5.6 Formation Evaluation & Management, 3 Production and Well Operations, 3 Production and Well Operations, 5 Reservoir Desciption & Dynamics, 5.6.3 Pressure Transient Testing
- Pressure Transient Analysis, Cloud Storage, Big Data, Cloud Computing, Injection
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- 173 since 2007
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|SPE Member Price:||USD 7.00|
|SPE Non-Member Price:||USD 23.00|
Pressure transient analysis provides useful information to evaluate injection induced fracture geometry, permeability damage near wellbore and pressure elevation in injection zone. Manual analysis of pressure data after each injection cycle could be subjective and time-consuming. In this study a cloud-based approach to automatically analyze pressure data will be presented, which is aimed to improve the reliability and efficiency of pressure transient analysis.
There are two fundamental requirements for the automated pressure transient analysis: 1) Pressure data needs to be automatically retrieved from field sites and fed to analyzer; 2) Analyzer can automatically select instantaneous shut-in pressure (ISIP), identify flow regimes, and determine fracture closure point. To meet these requirements and also take the advantages of cloud storage and computing technologies, a web based application has been developed to pull real time injection data from any field sites and push it to a cloud database. Besides analyzing any existing pressure data in the cloud database, a built-in pressure transient analyzer can also detect any real-time pressure data and perform pressure analysis automatically when required data is available.
The automated, cloud-based pressure transient analysis has been applied to multiple injection projects. In general, the analysis results including permeability, fracture half length, skin factor, and fracture closure pressure are comparable to these yielded from manual analysis. The discrepancy is mainly caused by poor data quality. The inconsistent selections of ISIP and different slopes defined for G-function and flow regime analyses also contribute to the divergence. Overall, the automated pressure transient analysis provides consistent results as the exact same criteria are applied to the pressure data, and analysis results are independent on analyzer’s experience and knowledge. In addition, machine learning algorithms are applied to continuously refine the criteria and improve the quality of analysis results.
As data from oil/gas industry increases exponentially over time, automated data transmission, storage, analysis and access are essential to maximize the value of the data and reduce operation cost. The automated pressure transient analysis presented here demonstrates that cloud storage and computing combined with automated analysis tools is an optimal way to overcome big data challenges facing by oil/gas industry.
|File Size||2 MB||Number of Pages||20|
Al-Kaabi, A. U., & Lee, W. J. (1990, January 1). An Artificial Neural Network Approach To Identify the Well Test Interpretation Model: Applications. Society of Petroleum Engineers. doi: 10.2118/20552-MS.
Athichanagorn, S., & Horne, R. N. (1995, January 1). Automatic Parameter Estimation From Well Test Data Using Artificial Neural Network. Society of Petroleum Engineers. doi: 10.2118/30556-MS.
Baldwin, J. O., & Norris, S. O. (1992, January 1). Software Showcase: Pressure Transient Analysis Programs. Society of Petroleum Engineers. doi: 10.2118/24461-MS.
Buhidma, I. M., Chu, W. C., & Singh, P. K. (1992, January 1). The Use of Computers in Pressure Transient Analysis. Society of Petroleum Engineers. doi: 10.2118/24730-MS.
Dastan, A., & Horne, R. (2010, August 1). Significant Improvement in the Accuracy of Pressure-Transient Analysis Using Total Least Squares. Society of Petroleum Engineers. doi: 10.2118/125099-PA.
Deng, Y., Chen, Q., & Wang, J. (2000, January 1). The Artificial Neural Network Method of Well-Test Interpretation Model Identification and Parameter Estimation. Society of Petroleum Engineers. doi: 10.2118/64652-MS.
Denney, D. (2011, September 1). Automated Pressure-Transient Analysis - Use Smart Technology. Society of Petroleum Engineers. doi: 10.2118/0911-0051-JPT.
Gringarten, A. C. (1986, January 1). Computer-Aided Well Test Analysis. Society of Petroleum Engineers. doi: 10.2118/14099-MS.
Guo, Y., Mohamed, I., Abou-Sayed, O.. J Petrol Explor Prod Technol (2018). https://doi.org/10.1007/s13202-018-0536-2.
Home, R. N. (1994, July 1). Advances in Computer-Aided Well Test Interpretation. Society of Petroleum Engineers. doi: 10.2118/24731-PA.
Houze, O. P., & Allain, O. F. (1992, January 1). A Hybrid Artificial Intelligence Approach in Well Test Interpretation. Society of Petroleum Engineers. doi: 10.2118/24733-MS.
Kumoluyi, A. O., Daltaban, T. S., & Archer, J. S. (1995, December 1). Identification of Well-Test Models by Use of Higher-Order Neural Networks. Society of Petroleum Engineers. doi: 10.2118/27558-PA.
McClure, M. W., Jung, H., Cramer, D. D., & Sharma, M. M. (2016, August 1). The Fracture-Compliance Method for Picking Closure Pressure From Diagnostic Fracture-Injection Tests (see associated supplementary discussion/reply). Society of Petroleum Engineers. doi: 10.2118/179725-PA.
McLennan, J. D., & Roegiers, J.-C. (1982, January 1). How Instantaneous are Instantaneous Shut-In Pressures? Society of Petroleum Engineers. doi: 10.2118/11064-MS.
Menouar, N., Liu, G., & Ehlig-Economides, C. (2018, October 16). A Quick Look Approach for Determining Instantaneous Shut-in Pressure ISIP and Friction Losses from Hydraulic Fracture Treatment Falloff Data. Society of Petroleum Engineers. doi: 10.2118/191465-18IHFT-MS.
Rees, H. R., Foot, J., & Heddle, R. (2011, January 1). Automated Pressure Transient Analysis with Smart Technology. Society of Petroleum Engineers. doi: 10.2118/144327-MS.
Smart, C. R., & Fertl, W. H. (1988, January 1). The Well-Site Real-Time Formation Pressure Transient Analysis System. Society of Petroleum Engineers. doi: 10.2118/17320-MS.
Sultan, M. A., & Al-Kaabi, A. U. (2002, January 1). Application of Neural Network to the Determination of Well-Test Interpretation Model for Horizontal Wells. Society of Petroleum Engineers. doi: 10.2118/77878-MS.
Willingham, J. D., Tan, H. C., & Norman, L. R. (1993, January 1). Perforation Friction Pressure of Fracturing Fluid Slurries. Society of Petroleum Engineers. doi: 10.2118/25891-MS.