Application of Time-Series Analysis to Induced Gamma Ray Spectroscopy Logs From Two Cold Lake Heavy-Oil Observation Wells

Abstract

Summary Induced gamma ray spectroscopy (IGRS) logs from two cyclic-steam-stimulation observation wells in Cold Lake were analyzed to determine the vertical resolution and repeatability of data derived from gamma rays of inelastic and capture neutron reac tions. Time-series analysis, a technique that uses the Fourier representation of the log data, was used to quantify the vertical resolution and the signal/noise characteristics of various IGRS log curves and to compute the coherence vs. spatial frequency of data collected on multiple IGRS passes. The coherence function ranges from 1.0 for perfect repeatability to 0.0 for incoherent noise. Because no real variations in the measured data were expected for the 12- to 24-hour data-collection period, any deviation of the coherence function from 1.0 is attributed to incoherent noise. Generally, coherence is high at low frequencies (large vertical scales) and low at high fre quencies (small vertical scales). By noting the frequency at which the coherence level decreases to the expected value of random noise, we can quantify the vertical resolution of a log curve. Data analysis from these wells indicates that both the vertical resolution and repeatability of individual capture and inelastic curves differ. We found that the H-yield and capture curves have the highest vertical resolution (≈0.3 m) and the best signal/noise ratios (FSN≈30:1). In contrast, the capture Ca and Si yields are of significantly lower quality (FSN≈2:1). Only a small difference exists between the vertical resolution of the inelastic C (1.0 m) and O (1.3 m) yields, but the FSN the O yield is only one-half that of the C yield. Fortunately, the vertical resolution and the repeatability of the C/O ratio, FCO, are determined primarily by the quality of the inelastic C data. Specific to our application of monitoring heavy-oil saturations during cyclic-steam stimulation in a relatively uniform, high-porosity unconsolidated sand, we developed a simplified FCO interpretation that does not rely on the elemental yields from capture data. This linear model permits us to assess the uncertainties in computed saturations from the statistical noise in the inelastic FCO data.