Signal Estimation Technology Inc. Maher S. Maklad Optimal Resolution of Noisy Seismic Data Resolve++

Signal Estimation Technology Inc. Your team is under constant pressure to extract the most information from corporate assets as accurately and swiftly as possible. Resolution Optimization: Motivation This information provides the foundation on which your business makes decisions. These decisions are based on a perception of reality. The result of these decisions depends on the accuracy of the perception. How to use seismic attributes to enable more informed decisions for the identification, reduction and management of risk while maximizing reward? One answer is to investigate both standard and alternative interpretation workflows available to determine ways of validating and/or improving upon current practices.

Signal Estimation Technology Inc. Produce a geologically faithful higher resolution seismic image. Resolution Optimization: Goals & Rewards The Rewards: More accurate and informative seismic attributes. Our Goal: Improved horizon picking. More accurate maps. Easier detection of important geologic features (faults, wedges, etc.).

Signal Estimation Technology Inc. Anatomy of Seismic Data = Consists of several components: SEISMIC Seismic (t) = Wavelet (t) * Reflectivity (t) + noise (t) Convolutional Model Seismic attribute analysis uses information extracted from the seismic data or its constituents. Seismic Response Time Energy Source Wavelet * Earth Reflectivity + Noise

Signal Estimation Technology Inc. Earth Filter = Seismic Response Earth Reflectivity Noise + Time Noise Attenuation Observations: Signal-to-Noise Ratio (SNR) is often not stressed. * Consequences: The two amplitude maps below show the impact of removing noise on interpretation. GCWS_top Amplitude map BeforeAfter

Signal Estimation Technology Inc. Resolution Optimization…..procedure Seismic Data Estimate signal power spectrum Decompose into a wavelet and a reflectivity component Estimate wavelet spectrum Estimate SNRDesign desired wavelet Operator = Desired wavelet / estimated input wavelet Strategy: Divide and conquer! Address the additive noise problem (Get the signal). Address the convolutional noise problem (Get the wavelet). Contrast with conventional techniques where operators are designed in the data domain – signal plus noise!

Signal Estimation Technology Inc. Resolution Optimization…..objectives Energy Source = Seismic response Earth Reflectivity Noise + Time * The objectives are: Estimate the wavelet in the presence of noise. Shape the wavelet according to SNR. Improve resolution while controlling noise Frequency (Hz) Magnitude (dB) Preserve the colour of the reflectivity. Well log generated Reflectivity Spectrum

Signal Estimation Technology Inc. Resolution Optimization ….results Before After

Signal Estimation Technology Inc. Before Resolve has made improvements in the following areas: Resolution Optimization ….validation Peak Frequency Increased peak frequency of the data from ~ 140 Hz to > 250 Hz. After Made the spectrum of the data follow the spectrum of the log generated reflectivity more closely providing confidence in the spectral gains, and the enhanced stratigraphic and structural interpretation that the Resolve data will enable. Increased the bandwidth of the data from ~ 200 Hz to ~ 300 Hz. Bandwidth Before After Well log generated Reflectivity Spectrum

Signal Estimation Technology Inc. Brute Stack Processors Final Stack Resolve on Brute Stack Resolve++ on Brute Stack versus Conventional Processing

Signal Estimation Technology Inc. Original Processed Volume Spectrally Shaped Volume Input to Resolve++

Signal Estimation Technology Inc. Original Processed Volume Spectrally Shaped Volume Output of Resolve++

Signal Estimation Technology Inc. Scanned Data Data from scanned seismic section

Signal Estimation Technology Inc. Scanned data after Resolve++

Signal Estimation Technology Inc. Resolve++ increases the resolution of seismic data without amplification of noise (i.e. constrained by SNR). Enables the extraction of better quality attributes for reservoir characterization. This results in a better spectral representation of earth reflectivity. More accurate interpretation of horizons and faults. A viable alternative to reprocessing old data. Conclusions