Integrated Workflow Reduces Uncertainty in Type Well Construction and Production Forecasting of Multi-Fractured Horizontal Wells
- Romain Lemoine (Texas A&M University) | John Lee (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 23-26 April, San Jose, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Analytical Scaling, Unconventional Resources, Type Wells, Integrated Workflow, Production Forecasting
- 8 in the last 30 days
- 146 since 2007
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A common purpose of type wells is to predict the production profiles of multi-fracture horizontal wells (MFHW) in a selected geologically similar area (GSA) in a resource play. This paper presents a unique workflow that leads to type well production forecasts as analytically scaled rate-time profiles for an identified range of reservoir and completion properties.
The workflow uses production data from all MFHW's in a GSA to build more representative type wells. First, we sort and bin the wells, accounting for observed flow regimes (transient and boundary-dominated flow). We then extract physical properties from the data using curve-matching techniques. Using these properties, we can forecast the production profile of any well already in BDF. For wells still in the transient flow regime and for undrilled wells, we probabilistically forecast performance using distributions of observed properties (such as permeability and fracture length) from existing wells in BDF and Monte Carlo simulation.
We demonstrate that the analytical scaling factors resulting from type-curve matching can be used to construct a probability distribution of production forecasts from type wells. We can scale the type well production profiles to a given set of reservoir and completion properties, including observed average sets of properties from well analyzed or based on altered completion designs. We illustrate the workflow with a data set of 126 MFHW gas wells in the Denton County, Texas, Barnett shale. Using information extracted from this data set, we forecasted production for undrilled wells and validated the forecasts using a sequential accumulation plot with information from10 analog wells.
Type wells are important tools for decision makers and engineers to determine economic feasibility of proposed development projects and estimate reserves. As opposed to purely empirical methods used for type well construction, our analytically-based workflow provides an integrated GSA characterization, reliable production forecasts and better tools for decision making with reduced uncertainty.
|File Size||2 MB||Number of Pages||20|
Arps, J.J. 1945. Analysis of Decline Curves. 10.2118/945228-G.
Caldwell, R.H., Heather, D.I. 1991. How To Evaluate Hard-To-Evaluate Reserves (includes associated papers 23545 and 23553). Journal of Petroleum Technology 43 (08): 998-1003 10.2118/22025-PA.
Chaudhary, N.L., Lee, W.J. 2016. Detecting and Removing Outliers in Production Data to Enhance Production Forecasting. 2016/5/10/. 10.2118/179958-MS.
Collins, P.W., Badessich, M.F., Ilk, D. 2015. Addressing Forecasting Non-Uniqueness and Uncertainty in Unconventional Reservoir Systems Using Experimental Design. 2015/9/28/. 10.2118/175139-MS.
Duong, A.N. 2010. An Unconventional Rate Decline Approach for Tight and Fracture-Dominated Gas Wells. 2010/1/1/. 10.2118/137748-MS.
Fetkovich, M.J., Vienot, M.E., Bradley, M.D.. 1987. Decline Curve Analysis Using Type Curves: Case Histories. 10.2118/13169-PA.
Freeborn, R., Russell, B. 2016. Creating More-Representative Type Wells. 10.2118/175967-PA.
Freeborn, R., Russell, B., Keinick, W.E. 2012. Creating Analogs, Fact and Fiction. 2012/1/1/. 10.2118/162630-MS.
Fulford, D.S., Blasingame, T.A. 2013. Evaluation of Time-Rate Performance of Shale Wells using the Transient Hyperbolic Relation. 2013/11/5/. 10.2118/167242-MS.
Groulx, B. 2015. Understanding Type-well Curve Complexities & analytic Techniques: Reservoir, Evaluation, and Production Optimization Luncheon (Reprint). https://www.verdazo.com/wp-content/uploads/2016/03/VISAGE-SPE-Presentation-2015-12-01-Calgary.pdf.
Holdaway, K. 2014. Harness Oil and Gas Big Data with Analytics : Optimize Exploration and Production with Data Driven Models. Somerset, UNITED STATES, John Wiley & Sons, Incorporated (Reprint). http://ebookcentral.proquest.com/lib/tamucs/detail.action?docID=1686621.
Ibrahim, M.H., Wattenbarger, R.A. 2006. Analysis of Rate Dependence in Transient Linear Flow in Tight Gas Wells. Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 2006/1/1/. 10.2118/100836-MS.
Ilk, D., Rushing, J.A., Perego, A.D.. 2008. Exponential vs. Hyperbolic Decline in Tight Gas Sands: Understanding the Origin and Implications for Reserve Estimates Using Arps’ Decline Curves. 2008/1/1/. 10.2118/116731-MS.
Jarvie. 2012. Shale resource systems for oil and gas: Part 1—Shale-gas resource systems. J. A. Breyer (Shale reservoirs—Giant resources for the 21st century: AAPG Memoir 97): 69–87 10.1306/13321446M973489.
Lacayo, J., Lee, J. 2014. Pressure Normalization of Production Rates Improves Forecasting Results. SPE Unconventional Resources Conference, The Woodlands, Texas, USA, 2014/4/1/. 10.2118/168974-MS.
McLane, M., Gouveia, J. 2015. Validating Analog Production Type Curves for Resource Plays. 2015/9/2/. 10.2118/175527-MS.
Murtha, J.A. 1997. Monte Carlo Simulation: Its Status and Future. Journal of Petroleum Technology 49 (04): 361-373 10.2118/37932-JPT.
SPEE. 2011. Guidelines for the Practical Evaluation of Undeveloped Reserves in Resource Plays: Monograph 3 (Reprint). www.spee.com.
Valko, P.P., Lee, W.J. 2010. A Better Way To Forecast Production From Unconventional Gas Wells. 2010/1/1/. 10.2118/134231-MS.
Wattenbarger, R.A., El-Banbi, A.H., Villegas, M.E.. 1998. Production Analysis of Linear Flow Into Fractured Tight Gas Wells. 1998/1/1/. 10.2118/39931-MS.