Qualitative QC at Wells and Maps for Inversion Predictions

Qualitative QC at Wells (1D) and on Maps = Comparison of inversion results to well logs Quantitative QC / Cross-plotting = Cross-plotting well log and inverted elastic properties Seismic Inversion Workflow – Inversion Interpretation = Using inversion results to define reservoir properties Inversion Interpretation – Porosity = Estimating porosity from inverted P-impedance

Linear regression from well logs = Simple methodology for porosity estimation Probabilistic Neural Network prediction method = Use of Emerge technology for porosity prediction Mean porosity result extraction = Estimation of 3D porosity volume around reservoirs Simultaneous inversion results analysis = Creation of 3D lithology volume using Vp/Vs ratio

Lithology volumes determination = Relevant output from elastic inversion Detecting lateral variation of attributes = Calculating maps of inverted elastic properties Reservoir property definition = Using inversion results to define porosity and lithology volumes Drawing polygons for facies separation = Obtaining discrimination between different lithologies

<p>P-impedance and Vp/Vs ratio = Parameters used for discriminating between coal and Pay sands Creation of 3D lithology volume = Result of applying discrimination to simultaneous inversion results Ip-Vp/Vs elastic domain polygons = Simplest way to obtain facies discrimination Probabilistic classification technique = Sophisticated method for facies discrimination</p> Signup and view all the answers

<p>Inversion Calibration = Fine-tuning inversion parameters Seismic Inversion Workflow Stage 3 = Exploring Quality Control of inversion results Adjusting a single weight or introducing an extra misfit function = Can disrupt the balance and affect all parameters Selecting the Inversion Type and required Parameters = Part of calibration process before running full inversion</p> Signup and view all the answers

<p>Scaling the wavelet = Part of calibrating inversion parameters Reviewing the inversion parameter testing = Done in module learning objectives Examining the target zone for Inversion Analysis = Before running full inversion Generating reliable subsurface models = Outcome of executing inversion process</p> Signup and view all the answers

<p>Inversion Parameters (Jason Example) = Variability of solution in seismic inversion List of parameters for Simultaneous inversion = Related to seismic, initial model, lateral, and vertical variability Desired balance between data accuracy and model smoothness = Overall goal in tuning inversion parameters Global relation to seismic, the initial model, and variability = Parameters in Jason software example</p> Signup and view all the answers

<p>Using inversion results for porosity and facies interpretation = Module learning objective Inversion preparation stage completion = Start of the actual inversion process Achieving reliable subsurface models = Focus during calibration and run stage Fine-tuning inversion parameters = Goal during calibration phase</p> Signup and view all the answers

<p>Closely examining the target zone for Inversion Analysis = Critical step before full inversion run Balancing data accuracy and model smoothness = Key in achieving quality inversion results Calibrating inversion parameters before full run = Ensures reliability and accuracy in subsurface modeling Disruption from adjusting a single weight or introducing extra misfit function = Impact on all parameters in seismic inversion</p> Signup and view all the answers

<p>Fine-tuned inversion parameters = Result of calibration process Reliable subsurface models = Generated after executing the inversion process Quality Control of inversion results exploration = Part of Seismic Inversion Workflow Stage 3 Balance between data accuracy and model smoothness = Desired outcome in tuning parameters</p> Signup and view all the answers

<p>Limited Resolution = Difficulty in accurately resolving thin layers and subtle variations in subsurface properties Non-Uniqueness = Challenge in determining a unique solution due to multiple combinations of subsurface properties producing similar seismic responses Seismic data quality = Issues related to noise, acquisition limitations, and incomplete coverage affecting the reliability of inversion results Handling Heterogeneity = Difficulty in accurately capturing and modeling the variations in lithology, fluid content, and other properties in the subsurface</p> Signup and view all the answers

<p>Utilizing advanced algorithms = Addressing challenges by employing sophisticated computational methods Integrating multiple data sources = Enhancing inversion accuracy by combining different types of data for analysis Incorporating constraints from well logs = Mitigating uncertainties by utilizing additional information from borehole data Applying rock physics models = Improving inversion results through the use of theoretical models relating rock properties to seismic responses</p> Signup and view all the answers

<p>Determining optimal setting for each inversion parameter = Necessary step to achieve the best inversion results by understanding parameter interactions Quality control of final inversion result = Critical process to ensure that the outcome aligns with expectations Using inversion results for interpretation = Applying computed reservoir properties or creating lithology volumes for analysis purposes Thorough testing of interconnected parameters = Essential to evaluate the impact of different settings on the inversion outcome</p> Signup and view all the answers

<p>Sensitivity analyses = Assessing the impact of parameter changes on inversion results Quantitative QC = Comparing statistical attributes like correlation and NRMS Residuals = Difference between seismic data and synthetic trace generated from inverted properties Sparse model = Model with low Fcontrast that may accompany significant data mismatch</p> Signup and view all the answers

<p>Visually comparing well logs = Assessing differences between inverted and measured well logs QC on 2D Sections = Checking residuals to see what seismic energy has not been inverted Trial runs = Assessing impact of parameter changes on inversion results through iterative refinement Comparing initial and final models = Identifying imprint of initial model and appreciation of HF information brought by seismic</p> Signup and view all the answers

<p>Checking compliancy with seismic data = Verifying residuals, seismic-synthetic comparison, and relative attributes Spatial and temporal variations examination = Ensuring consistency of inversion results over the area of interest Verification of geological coherency = Examining spatial and vertical variations in relation to geology Comparing elastic properties with well logs = Verifying if resulting elastic properties match the data from well logs</p> Signup and view all the answers

<p>Absolute P-Impedance, S-Impedance, Vp/Vs ratio = Main results to be checked qualitatively or quantitatively Density = Main result to be checked in some cases for compliancy with expectations Seismic-Synthetic comparison = Comparison used to verify if inversion results match seismic data Relative attributes (without low frequency model) = Attributes compared qualitatively or quantitatively to assess inversion quality</p> Signup and view all the answers

<p>Thorough testing of interconnected parameters = Testing effect of one parameter at a time for optimal setting determination Comparison of synthetic and seismic traces = Control effect by visually comparing differences between synthetic and seismic traces Integration of QC tools into software = User control by utilizing integrated tools to assess parameter effects Achieving a sparse model = Effect on misfit where achieving this may accompany significant data mismatch</p> Signup and view all the answers

<p>Qualitative QC on cross-sections, maps, wells = Comparison between two sets of data to ensure quality compliance Statistical attributes calculation and comparison = Quantitative assessment through statistical attributes like correlation and NRMS Verification of spatial and vertical variations coherence with geology = Ensuring consistency of results in relation to geological features Detection of unexpected anomalies in elastic properties = Identifying any anomalies that are not expected based on the geological context</p> Signup and view all the answers

<p>Determining optimal setting for each inversion parameter = Achieving best results through thorough testing to see effect on result Applying selected inversion parameters to whole area = Carrying out careful quality control to ensure consistent results over entire area of interest Examining inversion results at well locations = Checking quality control at specific locations for verification of geological coherency Performing sensitivity analyses and trial runs = Assessing impact of parameter changes through iterative refinement based on feedback</p> Signup and view all the answers

<p>Presence of primary energy in residuals display = Indication that inversion may be too constrained to model all seismic data Comparing initial model to final result = Understanding imprint of initial model and appreciating HF information brought by seismic Checking if there is an imprint of initial model in final result = Verifying if characteristics from initial model are present in final inversion output Detection of any unexpected anomalies in elastic properties = Identifying anomalies that are not expected based on geological context</p> Signup and view all the answers

<p>Inversion parameters selection = Applying optimal settings after determining them through thorough testing Quality control examination at various stages = Ensuring consistency of results by verifying different aspects like spatial variations Geological coherency verification = Checking if results align with expected geology features Quantitative assessment through statistical attributes = Calculating statistical attributes like correlation for numerical comparison</p> Signup and view all the answers

<p>Connection between misfit functions = Minimizing one misfit function may lead to increased misfit in others. Primary energy visibility in residuals = Indicates possible issues with AVO response or alignment in seismic data Elastic properties verification = Ensuring that resulting elastic properties align with data from well logs Sparse model characteristics = Often accompanies significant data mismatch despite achieving low Fcontrast</p> Signup and view all the answers