Paper No. 13
Presentation Time: 1:30 PM-5:00 PM
CALCULATING EQUIVALENT FRACTURE NETWORK PERMEABILITY OF MULTI LAYER COMPLEX NATURALLY FRACTURED RESERVOIRS
BABADAGLI, Tayfun, U of Alberta, Edmonton, AB T6G 2R3, Canada and JAFARI, Alireza, ershaghi@usc.edu
Modeling naturally fractured reservoirs (NFRs) requires an accurate representation of fracture network permeability. Conventionally, logs, cores, seismic, and pressure transient tests are used as data base for this. Our previous attempts showed that a strong correlation exits between the fractal parameters of 2‑D fracture networks and their permeability (SPE113618). We had also showed that 1‑D well (cores‑logs) and 3‑D reservoir data (well test) may not be sufficient in fracture network permeability (FNP) mapping and 2‑D (outrop) characteristics are needed (SPE124077). This paper is an extension of these studies where only 2‑D (single layer, uniform fracture characteristics in z‑direction) representations were used. In this paper, we considered a more complex and realistic 3‑D network system. 2‑D random fractures with known fractal and statistical characteristics were distributed. Variation of fracture network characteristics in the z‑direction was presented by a multi layer system representing ten different facieses with different fracture properties. Wells were placed in different locations of the model to collect 1‑D fracture density, multi‑well pressure transient data. In addition, twelve different fractal and statistical properties of the network of each layer were measured. The equivalent FNP was calculated using a commercial software package as the base case. Using available 1‑D, 2‑D, and 3‑D data, multivariable regression analyses were performed to obtain equivalent FNP correlations for many different fracture network realizations. The derived equations were validated against synthetic and natural fracture networks and conditions at which 1‑D and 3‑D are sufficient to map fracture network permeability were determined. Also performed was an analysis on the determination of minimum number wells to be logged, cored, and tested using an ANN analysis. Importance of the inclusion of 2‑D data in the correlations was discussed.