In the past few years, we have observed the introduction of smart technologies that adapt themselves to specific needs of individual users. There are many mobile and web-based services with learning capabilities that play the role of a personal assistant in our daily life. The foundation of this new class of services is a paradigm shift from intensive computational modeling and simulation of complicated phenomena toward data driven analytics. The oil and gas industry with the uncertainties convoluted into our measurements and understanding of the subsurface should not be excluded from this recent paradigm shift. Data driven analytics have proven to be a powerful alternative to conventional numerical and analytical solutions. In their advanced form, data driven technologies may be used as comprehensive management tools of oil and gas assets. In this paper, we study Hilight field in Powder River Basin, a mature field with large number of wells. Lack of sufficient dynamic data such as flowing pressure for mature fields is common among these types of fields. Conventional data analyses impose a challenge in the absence of time-variant field measurements in additional to production history. Acquisition of a comprehensive data set for oil and gas assets, in general, is a costly luxury that is not financially feasible for all investment budget ranges. Data-driven approach along with pattern recognition techniques can introduce a potential solution to this challenging task and extract practical and valuable insights which can be vital to identification, planning and developments of assets and plays. In this work, we analyze data from nearly 400 wells with partial completion and workover data. Well logs for only 15 wells is accessible providing less than 10% petrophysical data attributes over the entire well sets. Available production rate history for 185 wells starts from June 1969 and extends until April 2012. The information value of this dataset is investigated through a multi-step workflow. The workflow includes reservoir delineation and geological modeling, volumetric reserve and recovery factor estimations, production decline curve analysis, fuzzy pattern recognition (FPR) analysis and key performance indicators (KPI) analysis. FPR analysis provides time-laps spatial patterns, enabling us to qualitatively study the reservoir depletion and fluid flow in Hilight field. The result of these analyses has been used to identify the depletion distribution over time and sweet spots for infill locations. KPI analysis identifies relative influence of different parameters on hydrocarbon production. Top-Down Model is developed and used for field development planning and economic analysis on proposed new wells. The workflow has a minimal computational footprint compared to conventional methods. It has been demonstrated how these data driven techniques can be employed as a guide toward an improved reservoir management and planning.
[1]
Shahab D. Mohaghegh,et al.
An Integrated Technique for Production Data Analysis with Application to Mature Fields
,
2007
.
[2]
Shahab D. Mohaghegh,et al.
Reservoir Simulation and Modeling Based on Pattern Recognition
,
2011
.
[3]
M. Erwig.
The graph Voronoi diagram with applications
,
2000
.
[4]
Shahab D. Mohaghegh,et al.
Reservoir simulation and modeling based on artificial intelligence and data mining (AI&DM)
,
2011
.
[5]
Randy Lafollette,et al.
Practical Data Mining: Analysis of Barnett Shale Production Results With Emphasis on Well Completion and Fracture Stimulation
,
2012
.
[6]
Tony Hey,et al.
The Fourth Paradigm: Data-Intensive Scientific Discovery
,
2009
.