ERM 2005 ERM 2005 Morgantown, W.V. Morgantown, W.V. SPE Paper # - PowerPoint PPT Presentation

ERM 2005 ERM 2005 Morgantown, W.V. Morgantown, W.V. SPE Paper # 98012 SPE Paper # 98012 Reservoir Characterization Using Reservoir Characterization Using Intelligent Seismic Inversion Intelligent Seismic Inversion Emre Artun, WVU Shahab D. Mohaghegh, WVU Jaime Toro, WVU Tom Wilson, WVU Alejandro Sanchez, Anadarko September 15, 2005

motivation > Reservoir Modeling Workflow Reservoir Modeling Workflow motivation > Exploration: A structural model of the reservoir can Seismic Surveys be attained. Exploration Some data can be obtained from wells ( i.e. well logs, cores, well tests … ) Drilling Geostatistical variogram models can be developed Reservoir with the available data to interpolate / extrapolate Characterization available well data to the entire field. Flow in that 3D reservoir can be modeled with Reservoir commercial reservoir simulators to predict Simulation reservoir performance. Field Development

motivation > Reservoir Characterization Reservoir Characterization motivation > Issues about the data and problems regarding data analysis - must be considered carefully in reservoir characterization. Geostatistical models become insufficient in dealing with - issues like uncertainty, large variety of scales, immense size of data, etc. As an alternate; our industry has realized the power of soft - computing tools, which are capable of dealing with uncertainty, imprecision, and partial truth.

motivation > Reservoir Characterization Reservoir Characterization motivation > Integrating all different types Ten-feet of data in an accurate and high-resolution reservoir model SEISMIC One of inches WELL LOGS Fraction of inches CORES

motivation > Reservoir Characterization Reservoir Characterization motivation > Due to its low resolution, seismic data is used only to attain - a structural view of the reservoir. However, its 3D coverage over a large area attracts engineers - to merge it more detailed characterization studies. SEISMIC LOGS Inverse modeling of reservoir properties from the seismic - data is known as seismic inversion .

Statement of the Problem Statement of the Problem 1. Does a relationship exist between seismic data and reservoir characteristics, beyond the structural relationship? 2. If such a relationship exists, can it be extracted through the use of soft computing tools, such as artificial neural networks? 3. How that tool should be designed to develop the most reliable correlation models? i.e. neural network algorithm, number and type of seismic attributes that should be included... etc.

Previous Work Previous Work Chawathe et. al (1997) neural network neural network Surface Cross-well Gamma ray seismic seismic logs Reeves et. al (2002) In this study; vertical seismic profile (VSP) is incorporated - into the study as the intermediate scale instead of cross-well seismic. neural network neural network Surface VSP Well logs seismic

Vertical Seismic Profile (VSP) Vertical Seismic Profile (VSP) - Signal receivers are located in the borehole instead of - Signal receivers are located in the borehole instead of - Signal receivers are located in the borehole instead of surface, both down-going and up-going signals are received. surface, both down-going and up-going signals are received. surface, both down-going and up-going signals are received. Well Source surface rock layer boundary Receivers VSP resolution ≈ (Geophones) 2 * Surface seismic resolution

Statement of the Problem Statement of the Problem Using artificial neural networks is proposed to find a - desirable correlation between well logs and seismic data. Generalized regression neural network (GRNN) algorithm is used. Vertical seismic profile (VSP) is incorporated into the study - as the intermediate scale data. Another unique feature of this study was to develop and - work on a synthetic model, before dealing with real data.

Two-step Correlation Methodology Two-step Correlation Methodology Two steps of correlation 1) Correlation of surface seismic with VSP 2) Correlation of VSP with well logs Ste p 1 Ste p 2 Surface Well VSP Seismic Logs L o w fre que nc y Medium frequenc y H igh fre que nc y

Case 1 Case 1 Synthetic Model Synthetic Model

Description of the Model Description of the Model The model represents the Pennsylvanian stratigraphy of the - Buffalo Valley Field in New Mexico, including the gas- producing Atoka and Morrow formations. The geological complexity increases with depth; - 0.8 – 1.124 sec. (6,600 – 9,000 ft) interval has been used. Surface seismic and VSP responses have been computed - through a synthetic seismic line of 100 traces.

Description of the Model Description of the Model A synthetic seismic line with 100 traces, having 3 wells @ traces 20, 50, and 80. Trace 20 Trace 50 ( VSP well ) Trace 80

Available Data Available Data 1. Density and acoustic velocity distributions. 2. Surface seismic and VSP responses in the form of the following seismic attributes: Amplitude - Average energy - Envelope - Frequency - Hilbert transform - Paraphase - Phase -

Seismic Amplitude Distribution Seismic Amplitude Distribution

Case 1 – Case 1 – Synthetic Model ynthetic Model Correlation of surface seismic with VSP Correlation of surface seismic with VSP Step 1 Step 1 Correlation of VSP with well logs Step 2

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP) 2 Trace 57 3 e c a r T

Case 1 _ Step 1( Surface seismic VSP) Case 1 _ Step 1( Surface seismic VSP) Neural network design: Inputs Output Time + neural network Single VSP 7 surface attribute seismic attributes

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

Correlation Map Correlation Map Ste p 1 Ste p 2 Seven separate prediction models have Seven separate prediction models have Surface Well VSP Seismic Logs been developed for seven VSP attributes been developed for seven VSP attributes with the data of traces 32 + 57. Mo de l fo und with the data of traces 32 + 57. Now, let’s apply these models to the other Now, let’s apply these models to the other traces to have the predicted distributions. traces to have the predicted distributions. Surface Virtual Virtual Seismic VSP Well Logs

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP) FREQUENCY Actual Predicted

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP) PHASE Actual Predicted

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP) HI LBERT TRANSFORM Actual Predicted

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP) ENVELOPE Actual Predicted

Correlation Map Correlation Map Ste p 1 Ste p 2 Surface Well VSP Seismic Logs Mo de l fo und Step 1 - ACCOMPLISHED !.. Step 1 - ACCOMPLISHED !.. Surface Virtual Virtual Virtual Seismic VSP VSP Well Logs

Case 1 – Case 1 – Synthetic Model ynthetic Model Correlation of surface seismic with VSP Step 1 Correlation of VSP with well logs Correlation of VSP with well logs Step 2 Step 2

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs ) Density log has been selected as the target log, and data - of t-50 have been used in building network models. Instead of using actual values, the problem was converted - to a classification problem, because of observable averaged values of density log of t-50.

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs ) Class 1 Class 1 ρ ≈ 1.9 g/cc Class 2 Class 2 ρ ≈ 2.3 g/cc Class 3 Class 3 ρ ≈ 2.65 g/cc

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs ) Neural network design: Outputs Inputs Time Three neural network + Classes of 7 VSP Density attributes

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs ) r 2 = 0.82 Class 1 ρ ≈ 1.9 g/cc Class 2 ρ ≈ 2.3 g/cc Class 3 ρ ≈ 2.65 g/cc

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs ) Class 4 Class 2 Class 1 ρ ≈ 2.09 g/cc Class 3 Class 4 r 2 = 0.94

Correlation Map Correlation Map Ste p 1 Ste p 2 The prediction model for density has been The prediction model for density has been Surface Well VSP Seismic Logs developed with the data of trace 50. developed with the data of trace 50. Mo de l fo und Mo de l fo und Now, we can generate the cross-sectional Now, we can generate the cross-sectional density distribution. density distribution. Surface Virtual Virtual Seismic VSP Well Logs

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs ) DENSI TY Actual Predicted

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

Correlation Map Correlation Map Ste p 1 Ste p 2 Surface Well VSP Seismic Logs Mo de l fo und Mo de l fo und Step 2 - ACCOMPLISHED !.. Step 2 - ACCOMPLISHED !.. Surface Virtual Virtual Virtual Seismic VSP Well Logs Well Logs

Case 2 Case 2 Real Case Real Case The Buffalo Valley Field The Buffalo Valley Field

The Buffalo Valley Field, New Mexico The Buffalo Valley Field, New Mexico