Modeling, History Matching, Forecasting and Analysis of Shale Reservoirs performance Using Artificial Intelligence
- Shahab D. Mohaghegh (West Virginia University) | Ognjen Srecko Grujic (West Virginia University) | Saeed Zargari (Colorado School of Mines) | Amirmasoud Kalantari Dahaghi (West Virginia University)
- Document ID
- Society of Petroleum Engineers
- SPE Digital Energy Conference and Exhibition, 19-21 April, The Woodlands, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.8.2 Shale Gas, 5.5.2 Core Analysis, 1.6 Drilling Operations, 4.1.2 Separation and Treating, 7.6.6 Artificial Intelligence, 1.6.6 Directional Drilling, 6.1.5 Human Resources, Competence and Training, 5.5.8 History Matching, 5.6.4 Drillstem/Well Testing, 4.1.5 Processing Equipment, 5.6.1 Open hole/cased hole log analysis, 5.7 Reserves Evaluation, 5.1 Reservoir Characterisation, 7.6.4 Data Mining, 5.8.6 Naturally Fractured Reservoir, 5.1.5 Geologic Modeling, 3 Production and Well Operations, 5.8.4 Shale Oil, 1.2.2 Geomechanics
- 4 in the last 30 days
- 966 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Producing hydrocarbon from Shale plays has attracted much attention in the recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexity of the flow mechanism in the natural fracture and its coupling with the matrix and the induced fracture, impact of geomechanical parameters and optimum design of hydraulic fractures has not necessarily kept up with our interest in these prolific and hydrocarbon rich formations.
In this paper we discuss using a new and completely different approach to modeling, history matching, forecasting and analyzing oil and gas production in shale reservoirs. In this new approach instead of imposing our understanding of the flow mechanism and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. In other words, by carefully listening to the data from wells and the reservoir we developed a data driven model and history match the production process from Shale reservoirs. The history matched model is used to forecast future production from the field and to assist in planning field development strategies. We use the last several months of production history as blind data to validate the model that is developed.
This is a unique and innovative use of pattern recognition capabilities of artificial intelligence and data mining as a workflow to build a full field reservoir model for forecasting and analysis of oil and gas production from shale formations. Examples of three case studies in Lower Huron and New Albany shale formations (gas producing) and Bakken Shale (oil producing) is presented in this paper.
This article reviews a new reservoir simulation and modeling technology called Top-Down, Intelligent Reservoir Modeling (Top-Down Modeling - TDM - or short) as it is applied to shale formations with examples presented for New Albany, Lower Huron and Bakken Shales. The natural fractures in the shale contribute significantly to the production as the main conduit for reservoir permeability. Recent revival of interest in production from shale formations can be attributed to multi-stage hydraulic fractures. It is a known fact that success of these hydraulic fracturing procedures is directly related to their ability to reach and intersect the existing natural fractures in the shale formation. Mapping of the natural fractures in the shale formations have proven to be an elusive task. Even with most advanced logging technologies one can only detect the intersection of the natural fractures with the wellbore while the extent of these fracture beyond the wellbore and how they are distributed throughout the reservoir (between wells) remains the subject of research.
|File Size||4 MB||Number of Pages||14|