An Approach Towell Placement and Production in a Green Field – A Case Study
Chapter One
Objectives of the Study
The objectives of this study are to:
- Use geostatistical methods to distribute the petrophysical properties in building a 3D static model of the reservoir;
- Find the optimum number, type, and placement of wells required to develop the greenfield.
CHAPTER TWO
LITERATURE REVIEW
Geological Background of the Field
Reservoir Description
A-1 reservoir has a geometry comparable to channel sand with a gently west dipping slope. The structure has a maximum elevation difference of approximately 60ft. with a thickness of approximately 40 to 50ft. The average net-pay thickness is 20ft. The compressibility of the formation is approximately 3.0 x 10-6 psi-1. At 9290 reference depth, the initial saturation is estimated to be Soi = 77%, and Swi=23%. At the reference depth of 9290 ft., initial formation pressure is measured to be 4800 psia(Ertekin et al, 2001).
Porosity and Permeability
The A-1 formation consists of poorly-to-well-sorted Cretaceous Dakota J sands. The average effective porosity of the reservoir is 22%, with permeability ranging from 250 to 300 md. Figures 2.2 and 2.3 show the porosity and permeability distributions obtained from well-test data and core analysis. The permeability distribution in Figure 2.3 represents the permeability values along the longitudinal axis of the structure in the SW-NE direction. Permeability values along y-direction are reported to be approximately 80% of that along the x-direction. Hence, the flow directions are parallel to the SW-NE and SE-NW direction (Ertekin et al, 2001).
Literature Review
Introduction
Reservoir development requires huge investments. The decision for making these investments is based on several factors including reservoir performance predictions and well placement.
To achieve sound reservoir performance predictions, a reliable geological model is needed. Geostatistics attempts to improve predictions by developing different types of geological models. It uses methods that do not average important reservoir properties to construct a more realistic model of reservoir heterogeneity. Like the traditional deterministic approach, it preserves indisputable “hard” data where they are k nown and interpretative “soft” data where they are informative (Wilson et al. 2011).
In addition, petrophysical properties distribution is essential in building static models of the reservoir. One of the new technologies often incorporated into the process is geostatistics (Cressie and Hawkins 1980; Bueno et al. 2011). For more than a decade, geostatistical techniques have been an acceptable technology used to characterize petroleum reservoirs (Qi et al., 2007; Abdideh and Bargahi 2012; Esmaeilzadeh et al. 2013; Fegh et al., 2013).
Lastly, strategic well placement techniques are needed to maximize production. Therefore, the determination of the optimum number, type, and location of wells is very important in field development. This problem has recently gained more attention as a result of the increase in the world’s energy demand and increasing pressure to maximize recovery with minimum investments in oil fields. With easy onshore fields becoming rare and many of the world’s major fields reaching maturity, new expensive offshore developments are becoming more attractive. Hence the need for optimized reservoir performance is becoming more important every day(Nasrabadi et al.,2011).
Geostatistical Modeling of Property Distributions
The main aim of using geostatistics is to provide a fair assessment of geological uncertainty and a realistic model of variability. Geostatistics is used to generate many realizations of a 2D variable that represents the reservoir quality over the stratigraphic interval.
Variogram
Variogram is the most commonly used geostatistical technique for describing a spatial relationship. For reservoir modeling we need to express spatial variation of parameters, and the central concept controlling this is the variogram. The variogram captures the relationship between the difference in value between pairs of data points, and the distance separating those two points.
Numerically, this is expressed as the averaged squared differences between the pairs of data in the data set, given by the empirical variogram function, which is most simply expressed as:(Philip, 2012).
CHAPTER THREE
METHODOLOGY
DATA PROCESSING AND ANALYSIS
Data Acquisition
The data used for this project include: an iso porosity map ,an iso permeability map, structure and isopach maps of A-1reservoir downloaded via the American Association of Petroleum Geologists data page at http://archives.datapages.com/data/rmag/oilgasfields82/plumbush creek.htm. The maps were digitized to reproduce reservoir properties and the coordinates used for the analysis.
Outline of Methodology
The methods adopted for the analysis are as follows:
- Digitization of structural and Isopach maps of A-1 reservoir
- Estimation of Porosity, Permeability and Thickness at the unsampled locations
- Estimation of Original Oil in Place (OOIP)
- Reservoir dynamic modeling
Digitization of Structural and Isopach Maps of A-1 Reservoir
The structural and isopach maps were digitized and their corresponding coordinates were used to produce a digital terrain model (DTM) and contour map of reservoir A-1 as shown in Figure 3.1 and Figure3.2. The permeabilities and porosities and thickness values obtained from the isopermeability and isoporosity maps were used for the geostatistical modeling.
CHAPTER FOUR
RESULTS AND DISCUSSION
Introduction
Well placement was considered to be the key driver adopted to improve the performance of the reservoir modeled in this study with the purpose of maximizing the economic profitability of the green field. In respect of this, certain well and reservoir parameters such as, horizontal well length, vertical and horizontal permeabilities were varied to ascertain optimum hydrocarbon recovery. The critical evaluation of development strategies implemented to produce the greatest amount of hydrocarbons is described as follows:
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
Conclusion
The study demonstrated an approach to well placement in order to maximize production in a green field. The impact of varying well and reservoir parameters, i.e., horizontal well length and kv/kh, on hydrocarbon recovery was evaluated in this study. Based on the above analysis, the following conclusions can be made:
- The horizontal well length significantly affects the cumulative oil production of a petroleum reservoir. The results of the analysis indicated that a 3000 ft. long horizontal well produced the highest cumulative oil production as compared with that of 2000 ft. and 1500 ft.
- Furthermore, the variation of vertical to horizontal permeability anisotropy ratio (kv/kh) showed a tremendous improvement in cumulative oil production. From the analysis above, it was observed that the higher the kv/kh ratio the greater the cumulative oil production. At a kv/kh value of 0.55, a higher cumulative oil production was observed compared to a kv/kh ratio of 0.4, 0.2 and 0.1.
Recommendation
This project recommends that further research be carried out on different well placement approaches such as the angle of orientation and the azimuth for the drilling of the horizontal well.
REFERENCES
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