Seismic Data Acquisition Location Prediction System
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Solution Overview
Problem
Conventional geophysical seismic surveys face challenges in data quality and cost due to factors like sea currents, weather, and equipment issues, leading to incomplete or unusable data, necessitating costly repeat trips for infill data acquisition.
Innovation Solution
A method and system that model and predict seismic data acquisition based on various factors, including prevailing and predicted conditions, to determine optimal locations for data collection, thereby improving data quality and reducing costs by strategically planning seismic data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional seismic surveys are conducted to cover large areas, then the survey coverage area is improved, but data quality deteriorates due to factors like sea currents, weather, and equipment issues
Solution Approach 1:
The system performs preliminary actions by predicting acquisition factors (sea currents, weather, swell noise) before the seismic survey is conducted. This allows the system to pre-determine optimal survey locations and times, avoiding areas with predicted poor conditions, thereby maintaining high data quality while covering large areas without needing to repeat surveys
Solution Approach 2:
The system incorporates feedback by continuously monitoring actual acquisition factors during the survey and comparing them against predicted values. This feedback mechanism allows real-time adjustment of survey parameters and identification of data quality issues, enabling the system to maintain reliability while covering extensive areas
2Reliability
If repeat trips are conducted to infill data holes, then data quality is improved, but survey cost increases
Solution Approach 1:
The system performs preliminary prediction of acquisition factors to identify potential data quality issues before the survey is conducted. By pre-determining optimal survey locations and times, the system eliminates the need for costly repeat trips to infill data holes, as the initial survey is designed to avoid areas with predicted poor conditions
Solution Approach 2:
The system creates a virtual copy of the survey area by modeling acquisition factors and predicting data quality before the actual survey. This virtual model allows the system to plan the survey path to avoid problematic areas, eliminating the need for expensive physical repeat trips to the same locations
3Area of stationary object
If the survey duration is extended to cover more area, then survey coverage is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary prediction of acquisition factors to identify optimal survey locations and times in advance. This allows the survey to be conducted more efficiently by avoiding areas with predicted poor conditions, thereby covering more area in less time rather than extending the survey duration
Solution Approach 2:
The system dynamically adjusts the survey plan based on predicted and actual acquisition factors. By making the survey route and timing flexible rather than fixed, the system can optimize the path to cover maximum area in minimum time, avoiding unnecessary time consumption while maintaining comprehensive coverage
Data Source
AI summary
Methods, apparatuses, and systems are disclosed that assist in determining a location to acquire seismic data. In one embodiment, a method includes modeling acquisition of seismic data in a first location based on a first factor that impacts acquisition of seismic data in the first location. The method also includes generating a modeled attribute based on the modeling, and determining whether to acquire seismic data in the first location based on the modeled attribute or the first factor.