Basic Seismic Interpretation Pitfalls

Satyesh Bhandari

Exploration for oil & Gas is very cost intensive and high risk business, especially when all the easy to find oil is already discovered. All the new discoveries are expected in deep water areas. This requires all the more careful exploration inputs. The main role in hydrocarbon exploration is of seismic interpretation. The exploration success ratio is between 10 to 30 percent, which depends on various factors including experience.

Seismic interpretation involves analyzing seismic data to make inferences about the subsurface geology. It’s a most critical task in oil and gas exploration. Seismic interpretation for oil exploration presents specific challenges and pitfalls that can impact the accuracy of identifying potential hydrocarbon reservoirs.  

Seismic pitfalls, or spurious anomalies that are misinterpreted, can lead to drilling results that are disappointing compared to expectations. These pitfalls can originate from inadequate data acquisition, processing, and interpretation of subsurface geology. 

There are several pitfalls that can affect the accuracy and reliability of seismic interpretation leading to drilling of dry wells.

Data Quality Issues:

• Noise: Seismic data may contain various types of noise, such as acquisition-related noise, multiples, and processing artifacts. Seismic data in general is  contaminated by noise from various sources, which can obscure true signals. External sources of noise, such as traffic, industrial activities, and natural phenomena, can interfere with seismic signals. Failure to properly distinguish between signal and noise can lead to misinterpretations.
• Acquisition Footprint: Variations in acquisition parameters can create artifacts that complicate interpretation.
• Resolution Limitations: Limited resolution can lead to difficulty in distinguishing between closely spaced geological features. Inadequate resolution can make it difficult to detect thin layers or small-scale features critical for identifying reservoirs.

Processing Artifacts:

Errors introduced during data processing can mislead interpretations. Alignment of noise if interpreted as reservoir lead to failure.

• Migration Artifacts: Improper seismic migration can introduce artifacts that mislead interpretation.
• Multiple Reflections: Multiples can be misinterpreted as primary reflections, leading to incorrect mapping of subsurface structures.

Ambiguity in Data:

• Multiple Interpretations: The same seismic data can often be interpreted in different ways, leading to uncertainty in identifying true hydrocarbon-bearing structures.
• Fault Shadowing: Faults can create areas of poor data quality (shadows) that obscure underlying structures.

Complex Subsurface Geology:

• Structural Complexity: Complex geological settings, such as heavily faulted regions or salt domes, can be difficult to interpret accurately.
• Stratigraphic Complexity: Variations in sedimentary layers can complicate the interpretation of seismic reflections.
• Fault Interpretation: Misidentifying or inaccurately mapping faults can lead to incorrect predictions about trap integrity and hydrocarbon migration pathways. Salt Bodies: Salt structures can create complex velocity fields and obscure underlying features, complicating interpretation.
• Carbonate Reservoirs: These can have complex porosity and permeability distributions that are difficult to interpret from seismic data alone.

Misidentification of Horizons and Faults:

• Picking Errors: Errors in picking seismic horizons or faults can lead to incorrect geological models.
• Ghost Events: False seismic events caused by multiples (reflections from layers above the target) can be mistaken for real features.

Velocity Model Errors:

• Incorrect Velocity Models: Inaccurate velocity models used in seismic processing can distort the true position and shape of geological features. The structural lows can be misinterpreted as structural highs, which may be identified as prospect. Misestimations in the velocity model can distort depth imaging, leading to errors in the positioning and size of potential reservoirs.
• Depth Conversion Errors: Errors in converting time-based seismic data to depth-based data can lead to misinterpretations.
• Anisotropy Effects: Ignoring anisotropy in the subsurface can result in inaccurate velocity models and misinterpretations.

Structural pitfalls

• velocity pull-up or push-down: These can include false structures due to seismic noise and processing errors, such as velocity pull-up or push-down due to lateral variations in the overburden.

Stratigraphic Challenges:

• Stratigraphic Traps: Identifying subtle stratigraphic traps requires high-resolution data and careful interpretation, which can be challenging.
• Lateral Changes: Rapid lateral changes in lithology and fluid content can complicate seismic interpretation.

Bias and Subjectivity:

• Interpreter Bias: Personal biases and preconceived notions in general influences interpretation, leading to subjective and potentially incorrect conclusions. Interpretations can be influenced by prior knowledge or expectations, leading to biased conclusions. Many interpreters are emotionally attached to their interpretations and not ready to accept any alternate possible interpretation.
• Over-Reliance on Software: Blind trust in software algorithms without proper validation can lead to erroneous results. Many a times wrong input parameters may lead to wrong output by the software. Automated interpretation tools, if not properly calibrated and validated, can produce erroneous results.

Amplitude Interpretation:

These can include not understanding what can be inferred from seismic amplitudes for the target interval, focusing too much on a single attribute, or allowing an amplitude study to become a "sideshow".

• False Bright Spots: Bright spots on seismic data, often indicative of hydrocarbons, can be caused by other factors such as lithological changes or tuning effects.
• AVO (Amplitude Versus Offset) Anomalies: Misinterpreting AVO responses can lead to incorrect conclusions about fluid content and reservoir properties.

Inadequate Integration with Other Data:

• Lack of Correlation with Well Data: Failing to integrate seismic interpretation with well logs and other geological data can lead to incorrect interpretations and conclusions. It is a general tendency to ignore even hard data if it is not fitting to the preconceived model. Or on the other side trying  to fit wrong and erroneous data to the interpretation model.
• Ignoring Geological Context: Overlooking the broader geological context can result in misinterpretation of seismic features.
• Geological Model Consistency: Ensuring that seismic interpretations are consistent with geological and petrophysical models is crucial but can be challenging.

Limited Training and Experience:

• Inexperienced Interpreters: Lack of experience and training in seismic interpretation can lead to mistakes and oversights.
• Miscommunication: Poor communication among multidisciplinary teams can lead to misinterpretations and overlooked data. Working in isolation by different always lead to wrong interpretation.

Technological Limitations:

• Limitations of Seismic Acquisition Technology: The inherent limitations of seismic acquisition technology can impact data quality and interpretability.
• Computational Constraints: Insufficient computational resources can limit the ability to process and analyze seismic data effectively. Relying on outdated or inadequate software tools can limit the accuracy and effectiveness of seismic interpretation.

Environmental and Surface Conditions:

• Surface Conditions: Variations in surface conditions (e.g., topography, vegetation) can impact data acquisition and quality. The same is to be taken care while doing interpretation.
• Weather and Seasonal Effects: Weather and seasonal changes can introduce variability in seismic data.

Mitigating these pitfalls involves using high-quality seismic data, advanced processing techniques, rigorous quality control, thorough integration of multiple data sources (including well logs and geological models), continuous training and development for interpreters, and effective communication within multidisciplinary teams.