Conventional Logs for Fracture Detection & Characterization in One of the Iranian Field

Abstract

Abstract Fracture analysis is generally accepted as one of the main steps through better understanding and optimization of assessment to production in most of the carbonate reservoirs. Effects of fracturing on reservoir performance provides engineers and geoscientists with the information needed to make business decisions with a higher degree of certainty. There are a few methods, which give us tracks of the fractures and the main problem is that even these are obtained using considerable time and money. What is clear is the importance of these data. In the last decade the main obsession of reservoir professionals was to develop some methods to detect more exactly the fracture attributes as well as extending the domain of the obtained data by different ordinary practices among them borehole image logs, surface geological analysis, well tests, etc. In the current paper we will apply a method, by which we could estimate the position of the fractures and this method could be used for confirming the results of the borehole image logs or even used alone and we will show that the fracture positions would be determined as precise as any other available method(s). Five wells were selected in one of the Iranian gas reservoir for which we have already the result of seismic interpretation. Other information as some petrophysical logs (Sonic, Neutron Density, MSFL, RHOB, etc), mud loss and drilling data are available for all wells. One of these wells also has borehole image log. Applying these data we could determine the position of the fracture and roughly the fracture orientation. By determining the Wavelet detail coefficient of petrophysical logs, and velocity index, with considering to other data including Mud loss and Variable Density Log (VDL), we could establish the fracture positions along the wells. For fracture density determination in inter well space, first curvature analysis was done and, then fracture density along the wells correlated with structural curvature at well location. We use this correlation and Geo-statistical method for obtain fracture density map in the reservoir. Introduction Rock having low interstitial matrix porosities and correspondingly low permeability in the absence of fracture, may constitute commercial reservoir when fractures provide local short-range drainage for the matrix and effective transportation channels to the well bore. For this reason it is often desirable to obtain qualitative and quantitative indications of the presence, density and orientation of fracture system. Fracture zone play an important role in our understanding of fluid flow within the earth's crust, for example in hydrology, recovery of hydrocarbons or transport of contaminants. Knowledge of fracture distribution is also important for our understanding of fluid flow in the reservoir especially in fractured reservoir management, it is sure to be unsuccessful without good understanding to fracture characteristics. A lot of papers on fracture identification have been published so far. Through the implementation from the U.S. DOE's Slant-Hole Completion Test (SHCT) and other research, core fracture identification has achieved remarkable success. The imaging well logs developed by Schlumberger Company gave impetus to fracture identification. The methods of fracture identification by means of core and imaging log are direct and effective, but coring and imaging logging require great expenses. During reservoir developing, a lot of conventional well-logging (CWL) information is provided for us. It is important and beneficial to make full use of CWL information to identify fractures. In this paper we use a novel method which is represented by Sahimi & Hashemi for detection of fracture position along the well by using CWL. [13] However wavelet is a good tool in fracture detection along well but it seems that this method should be constrained by other data if we want to have more accurate result. In this paper we use several data sources such as conventional well log data, result of wavelet analysis on log data, Velocity index, Variable density log(VDL), Mud loss and Mud weight data, drilling and geological data and also petrophysical interpretation.