Estimating Permeability in Uncored Wells Using Modified Flow Zone Index

Abstract

Permeability estimation for un-cored wells is a classic issue. A simple model that widely used is using core porosity-core permeability cross plot to determine the linier regression. Then we estimate permeability in un-cored well after making adjustment for porosity log to porosity core. The difficulties using that method is most of cross plot did not show clear relationship (scatter data) due to effect of rock heterogeneity. Therefore another effort is needed by separate it based on rock type to get better relationship. Permeability itself is not depend only on porosity but also other properties like clay content, grain size, tortuosity and etc. Part of this phenomenon had been modeled by Carmen-Kozeny which illustrates strong dependency of permeability on average grain size, tortuosity and flow zone index. The conventional way to reduce data scatter is by using additional correlation parameters. Commonly shale content (Vsh) and reservoir facies are used to give reliable transform or regression analysis for estimating the permeability. Start with this concept; we try to simplify the correlation by modified the Carmen Kozeny eq. (used flow zone index term) using clay content as another parameter that influences permeability value. Because we assume that porosity and clay content are the most important properties that have significant effect on permeability. In this paper, we will describe permeability estimation for un-cored well as function of porosity and clay content using modified flow zone index-permeability cross plot. This cross plot has been test in three clastic reservoirs in Lower Sihapas formation either for consolidated or unconsolidated sandstone. The result shows this cross plot give better relationship compare to conventional cross plot and more simple transform to estimate permeability in un-cored well for input to geologic and reservoir simulation models. Introduction EMP Malacca Strait has develop reservoir integrated study which involves data review, G & G remodeling, reservoir characterization, reservoir modeling and prediction to identify the reliable hydrocarbon potential and developing a reliable reservoir model for choosing the optimum development plan for Lower Sihapas formation in three clastic reservoir1,2. One of the main subjects during the integrated study workflow is the reservoir rock characterization and core data analysis which includes the permeability transform to develop correlation between core data and log data. Permeability is one of the fundamental rock properties which represent the quality of a reservoir. The appropriate permeability value in each well is needed in order to represent the permeability distribution in a reservoir model. Usually the data that we have from cored well is very limited. Beside that, in order to get the data from each well we need to perform coring process that quite time consuming and require expensive laboratory measurements. For that reason, in term of practical use, we need a mathematical function that can represent the permeability value from each key well (cored well) by using the existing data (log data).

Research on Reservoir Flow Unit Classification at Block D404 of Xinzhan Oilfield in Songliao Basin

“…Flow zone index FZI is a unique parameter which combines structure, mineral geological characteristics and pore throat characteristics to determine pore geometry [14][15] . Therefore, it can accurately describes the reservoir heterogeneity.…”

Tight-Gas-Sand Permeability Estimation From Nuclear-Magnetic-Resonance (NMR) Logs Based on the Hydraulic-Flow-Unit (HFU) Approach

“…Well log data can be used to continuously record formation information; therefore, it was used to predict FZI. Altunbay et al (1997), Al-Ajmi and Holditch (2000), Babadagli and Al-Salmi (2002), Svirsky et al (2004), Jiao and Xu (2006), Rahmawan et al (2009) andHaghighi et al (2011) pointed out that FZI was related to conventional logs, such as gamma ray (GR), interval-transit-time log (AC) and deep lateral resistivity. FZI can be derived from conventional logs by us eof the multivariate statistical regression method.…”