Traditional seismic surveys miss 30-40% of subsurface hydrocarbon indicators because human interpreters cannot process the massive data volumes generated by modern acquisition systems, analyze complex fault patterns across multi-dimensional datasets, or detect subtle amplitude variations that indicate reservoir quality, forcing exploration teams to drill expensive confirmation wells based on incomplete geological understanding and accept discovery rates below 25% in frontier basins. iFactory's AI-powered seismic interpretation platform processes terabytes of 3D and 4D seismic data in hours instead of months, automatically identifying fault networks, stratigraphic traps, direct hydrocarbon indicators, and reservoir property variations with 92% accuracy, reducing interpretation time by 78% while detecting prospects that conventional analysis overlooks. Book a demo to see AI seismic interpretation for your exploration program.
Quick Answer
AI seismic interpretation achieves 92% accuracy in detecting hydrocarbon-bearing structures versus 60-75% for traditional manual interpretation, processes complete 3D seismic volumes in 72 hours versus 4-6 months, and identifies 35% more drillable prospects by detecting subtle geologic features invisible to conventional analysis. iFactory's machine learning platform analyzes seismic attributes, well log correlations, and production data simultaneously to rank prospects by probability of success, eliminating interpretation bias and dramatically improving exploration ROI through faster cycle times and higher discovery rates.
AI Seismic Interpretation
Process Years of Seismic Data in Days with 92% Detection Accuracy
See how iFactory's AI analyzes 3D seismic volumes automatically, identifying fault systems, stratigraphic traps, and direct hydrocarbon indicators faster and more accurately than traditional interpretation workflows.
92%
Detection Accuracy
78%
Faster Interpretation
How AI Seismic Interpretation Works
The workflow below shows the five-stage AI analysis process iFactory applies to seismic volumes, from data ingestion through automated interpretation to prospect ranking and drilling recommendations.
1
Seismic Data Ingestion & Quality Control
Complete 3D seismic volume loaded into AI platform: 2,400 square kilometers survey area, 1,200 inlines, 840 crosslines, 6-second two-way time depth, 4ms sample rate. Automated quality checks identify acquisition footprints, noise patterns, processing artifacts. Data normalized for amplitude consistency across survey area. Well log data from 18 offset wells integrated for calibration: sonic logs, density logs, resistivity, gamma ray. Seismic-to-well ties established at each well location.
2
Automated Horizon & Fault Interpretation
Machine learning algorithms trained on regional geology automatically pick 24 key stratigraphic horizons across entire seismic volume. Neural networks detect fault networks: 187 faults identified, throw measurements calculated, fault displacement patterns analyzed for hydrocarbon migration pathways. Structure maps generated for each horizon showing anticlines, synclines, fault blocks. Time-to-depth conversion applied using velocity model from well data. Structural interpretation completed in 12 hours versus 8-12 weeks for manual interpretation.
3
Seismic Attribute Analysis & DHI Detection
AI computes 40+ seismic attributes from amplitude data: instantaneous frequency, envelope, phase, similarity, curvature, spectral decomposition. Pattern recognition algorithms identify direct hydrocarbon indicators: bright spots (high-amplitude gas response), flat spots (gas-water contacts), amplitude versus offset anomalies, low-frequency shadows below gas accumulations. Each DHI classified by confidence level and calibrated against production results from analog wells. 47 potential DHI anomalies flagged for detailed review.
4
Reservoir Property Prediction from Seismic Inversion
Seismic inversion converts amplitude data to acoustic impedance volumes representing rock properties. AI predicts porosity, net-to-gross sand ratio, fluid saturation from inverted impedance using relationships learned from well log data. Machine learning model trained on 18 well logs predicts reservoir quality across undrilled areas. Porosity prediction accuracy validated at well locations: R-squared 0.84 correlation between predicted and measured values. High-quality reservoir fairways mapped showing zones with predicted porosity above 18% and hydrocarbon saturation above 60%.
5
Prospect Generation & Probability of Success Ranking
AI integrates structural interpretation, DHI detections, and reservoir property predictions to identify drillable prospects. Each prospect evaluated for five geological risk factors: trap presence (structural closure verified), reservoir quality (porosity prediction above threshold), seal integrity (cap rock continuity), source maturity (burial depth analysis), migration pathway (fault connectivity to kitchen). Probability of success calculated from risk factors weighted by regional analog performance. 12 high-graded prospects identified with POS above 35%, ranked by expected value considering reserve estimates and drilling costs.
Interpretation complete: 24 horizons mapped, 187 faults identified, 47 DHI anomalies detected, 12 drillable prospects ranked by POS. Top prospect: 42% POS, 15 MMboe estimated reserves, recommended for drilling. Analysis time: 72 hours total.
AI vs Traditional Seismic Interpretation: Accuracy Comparison
Every comparison below demonstrates measurable accuracy advantages of AI interpretation versus conventional manual workflows based on drilling results from exploration programs that tested both interpretation methods. Discuss AI seismic interpretation for your exploration program.
01
Fault Detection Completeness
Traditional Method: Manual fault interpretation on 2D seismic sections identifies major faults with throws above 50ms two-way time. Small faults below interpreter detection threshold missed. Typical result: 60-70% of fault network identified, minor faults that create compartmentalization and affect reservoir connectivity overlooked. Drilling encounters unexpected faults, well placement suboptimal relative to actual fault geometry.
AI Accuracy: Machine learning detects faults at all scales from 10ms throw upward by analyzing seismic coherence and discontinuity attributes across 3D volume. Validation against drilling results: AI identifies 94% of faults confirmed by well data versus 68% for manual interpretation. Complete fault network mapping enables accurate reservoir compartment identification and optimal well placement relative to structural complexity.
AI Accuracy: Machine learning detects faults at all scales from 10ms throw upward by analyzing seismic coherence and discontinuity attributes across 3D volume. Validation against drilling results: AI identifies 94% of faults confirmed by well data versus 68% for manual interpretation. Complete fault network mapping enables accurate reservoir compartment identification and optimal well placement relative to structural complexity.
02
Stratigraphic Trap Identification
Traditional Method: Stratigraphic traps (pinchouts, channels, reefs) difficult to map on seismic because subtle amplitude variations easily overlooked during visual interpretation. Interpreter relies on specific seismic lines that show best expression of feature, extrapolation between lines introduces uncertainty. Discovery rate for stratigraphic plays: 15-25% based on conventional seismic interpretation.
AI Accuracy: Pattern recognition algorithms trained on known stratigraphic accumulations detect similar seismic signatures across undrilled areas. System analyzes complete 3D volume without line-selection bias, identifies amplitude anomalies and geometries characteristic of channels, reefs, pinchouts. Validation: AI-identified stratigraphic prospects show 38% discovery rate, 2.5x improvement versus traditional interpretation, with reserve sizes averaging 35% larger due to better delineation of trap extent.
AI Accuracy: Pattern recognition algorithms trained on known stratigraphic accumulations detect similar seismic signatures across undrilled areas. System analyzes complete 3D volume without line-selection bias, identifies amplitude anomalies and geometries characteristic of channels, reefs, pinchouts. Validation: AI-identified stratigraphic prospects show 38% discovery rate, 2.5x improvement versus traditional interpretation, with reserve sizes averaging 35% larger due to better delineation of trap extent.
03
Direct Hydrocarbon Indicator Validation
Traditional Method: Interpreter identifies bright spots and flat spots as potential DHIs, but cannot quantitatively assess reliability. Many amplitude anomalies caused by lithology changes, tuning effects, or processing artifacts rather than hydrocarbons. Traditional DHI interpretation shows 40-60% success rate, meaning significant percentage of drilled DHI anomalies are dry holes, expensive false positives.
AI Accuracy: Machine learning trained on drilled DHI outcomes (successful versus unsuccessful) learns to discriminate true hydrocarbon responses from false positives based on amplitude behavior, frequency content, and AVO characteristics. AI DHI classification achieves 82% positive predictive value, meaning 82% of AI-flagged high-confidence DHIs confirmed by drilling versus 52% for conventional interpretation. Reduces dry hole risk by 58% when prioritizing AI-ranked DHI prospects.
AI Accuracy: Machine learning trained on drilled DHI outcomes (successful versus unsuccessful) learns to discriminate true hydrocarbon responses from false positives based on amplitude behavior, frequency content, and AVO characteristics. AI DHI classification achieves 82% positive predictive value, meaning 82% of AI-flagged high-confidence DHIs confirmed by drilling versus 52% for conventional interpretation. Reduces dry hole risk by 58% when prioritizing AI-ranked DHI prospects.
04
Reservoir Quality Prediction Accuracy
Traditional Method: Geophysicist estimates reservoir properties by qualitative assessment of seismic amplitudes and manual correlation to well logs. Porosity and net sand predictions highly uncertain in undrilled areas, often differ by 50-100% from actual values encountered in wells. Poor reservoir quality prediction leads to drilling marginal targets or missing high-quality reservoir zones.
AI Accuracy: Supervised learning models predict porosity and net-to-gross from seismic inversion results using training data from all available wells. Cross-validation shows AI porosity predictions within 15% of measured values at blind well test locations versus 40% error for traditional qualitative estimates. Accurate reservoir quality mapping focuses drilling on highest-quality reservoir fairways, improving average well productivity by 45% through better geological targeting.
AI Accuracy: Supervised learning models predict porosity and net-to-gross from seismic inversion results using training data from all available wells. Cross-validation shows AI porosity predictions within 15% of measured values at blind well test locations versus 40% error for traditional qualitative estimates. Accurate reservoir quality mapping focuses drilling on highest-quality reservoir fairways, improving average well productivity by 45% through better geological targeting.
05
Interpretation Bias Elimination
Traditional Method: Human interpreters bring cognitive biases to seismic analysis: confirmation bias (seeing what they expect based on preconceived geological model), anchoring bias (over-reliance on initial interpretation), availability bias (interpreting features similar to recently seen examples). Different interpreters produce different interpretations from same data, internal studies show 25-35% variation in prospect inventory between interpretation teams.
AI Accuracy: Machine learning applies consistent interpretation criteria across entire dataset without bias toward any particular geological model. Prospect identification based purely on data patterns matching training examples from successful discoveries. Eliminates interpreter-dependent variation, produces repeatable results. When multiple AI models trained independently on same data, prospect lists show 92% overlap versus 68% overlap between human interpretation teams, demonstrating superior consistency.
AI Accuracy: Machine learning applies consistent interpretation criteria across entire dataset without bias toward any particular geological model. Prospect identification based purely on data patterns matching training examples from successful discoveries. Eliminates interpreter-dependent variation, produces repeatable results. When multiple AI models trained independently on same data, prospect lists show 92% overlap versus 68% overlap between human interpretation teams, demonstrating superior consistency.
06
Interpretation Speed & Data Volume Handling
Traditional Method: Skilled geophysicist requires 4-6 months to complete full interpretation of 3D seismic survey: horizon picking, fault mapping, attribute analysis, prospect identification. Large data volumes overwhelm human processing capacity, interpreter forced to subsample data by analyzing every 10th or 20th line, potentially missing features between analyzed sections. Time pressure forces shortcuts that compromise interpretation quality.
AI Speed: Complete 3D volume interpretation delivered in 72 hours including all horizons, faults, attributes, and prospect ranking. System analyzes every trace in dataset without subsampling, ensures no features overlooked. 98% reduction in cycle time from seismic acquisition to drill-ready prospects enables rapid exploration decisions. Speed advantage particularly critical for competitive bid rounds where interpretation quality and turnaround time determine winning strategy.
AI Speed: Complete 3D volume interpretation delivered in 72 hours including all horizons, faults, attributes, and prospect ranking. System analyzes every trace in dataset without subsampling, ensures no features overlooked. 98% reduction in cycle time from seismic acquisition to drill-ready prospects enables rapid exploration decisions. Speed advantage particularly critical for competitive bid rounds where interpretation quality and turnaround time determine winning strategy.
AI Model Training & Calibration Methodology
AI seismic interpretation accuracy depends on proper training using regional well control and seismic-to-well calibration. iFactory's methodology ensures models learn geological patterns specific to target basin.
Training Data Assembly
Historical seismic surveys and well data compiled from target basin and analogous geological settings. Minimum requirement: 15-20 wells with complete log suites tied to seismic data. Training examples labeled: successful discoveries (hydrocarbon-bearing wells) and failures (dry holes, water-bearing zones). Seismic attributes extracted at well locations for supervised learning. Data augmentation through synthetic modeling increases training set size.
Quality control: Wells selected to represent full range of geological variability in basin: different reservoir types, trap styles, fluid types, depth ranges. Seismic-to-well ties validated by checkshot surveys or VSP data ensuring accurate time-depth calibration. Training data quality directly impacts model prediction accuracy, rigorous QC essential for deployment success.
Model Architecture Selection
Different AI architectures optimized for specific interpretation tasks. Convolutional neural networks for fault and horizon detection from 3D seismic volumes. Recurrent neural networks for sequence analysis in stratigraphic interpretation. Random forests and gradient boosting for classification tasks like DHI validation. Ensemble methods combining multiple models improve robustness versus single-model approaches.
Performance optimization: Hyperparameter tuning through cross-validation identifies optimal model configuration. Regularization techniques prevent overfitting to training data. Model complexity balanced against available training data volume. Transfer learning from global models pretrained on large multi-basin datasets accelerates training for basins with limited well control. Final model selection based on blind test performance on wells not used in training.
Validation & Accuracy Assessment
Model performance measured on held-out validation dataset never seen during training. Key metrics: fault detection recall (percentage of actual faults identified), DHI precision (percentage of flagged anomalies confirmed as hydrocarbons), porosity prediction RMSE (root mean square error versus measured values). Confusion matrix analysis quantifies false positive and false negative rates for classification tasks.
Deployment criteria: Model approved for production use only after achieving minimum accuracy thresholds validated on independent well data: 90% fault detection, 80% DHI precision, porosity RMSE under 2 porosity units. Continuous monitoring after deployment compares predictions to drilling results, triggers model retraining when new wells added or performance degradation detected. Feedback loop ensures model accuracy improves as more calibration data becomes available.
Interpretability & Explainability
AI predictions accompanied by confidence scores and uncertainty quantification. Visualization tools show which seismic features drive model decisions: attribution maps highlight data regions most influential for specific prediction. Uncertainty estimates based on model ensemble disagreement or dropout sampling during inference. Geoscientists review high-confidence predictions prioritized for drilling, interrogate low-confidence results requiring additional data.
Geoscience integration: AI serves as decision support tool augmenting geoscientist expertise rather than black-box replacement. Interpretable predictions build confidence in AI recommendations, enable geoscience oversight of model outputs. Human-in-the-loop workflow where geophysicists validate AI interpretation and provide feedback improves model performance through active learning. Final drilling decisions combine AI analysis with geoscience judgment, economic evaluation, and strategic considerations.
Platform Capability Comparison
Traditional seismic interpretation workstations provide manual picking tools but lack automated AI analysis. Generic machine learning platforms require extensive customization for geophysical applications. iFactory differentiates through purpose-built AI models trained on exploration datasets, automated end-to-end workflows from seismic input to prospect ranking, and continuous learning from drilling feedback. Book a comparison demo.
Scroll to see full table
| Capability | iFactory | Traditional Workstation | Petrel E&P | Paradigm | Generic ML Platform |
|---|---|---|---|---|---|
| Automation & Speed | |||||
| Automated horizon picking | AI auto-tracks 24+ horizons | Manual picking only | Semi-auto with seed points | Semi-auto with seed points | Custom dev required |
| Fault network detection | ML detects all faults 10ms+ | Manual interpretation | Ant-tracking attributes | Fault enhancement tools | Custom development |
| Complete interpretation time | 72 hours automated | 4-6 months manual | 6-8 weeks semi-auto | 6-8 weeks semi-auto | Variable, custom workflow |
| AI & Machine Learning | |||||
| DHI detection & validation | AI with 82% precision | Visual interpretation | Attribute analysis only | Attribute analysis only | Trainable with effort |
| Reservoir property prediction | ML from seismic inversion | Manual correlation | Geostatistical methods | Stochastic modeling | Custom model build |
| Drilling outcome learning | Auto-retrain from results | Not available | Not available | Not available | Manual retraining |
| Decision Support | |||||
| Prospect probability of success | AI-calculated from 5 risk factors | Manual assessment | User-entered values | User-entered values | Not available |
| Prospect ranking by expected value | Automated EV calculation | Manual spreadsheet | Economic module | Economic module | Not available |
| Uncertainty quantification | Model ensemble confidence | Interpreter judgment | Monte Carlo simulation | Monte Carlo simulation | Custom implementation |
Based on publicly available product documentation as of Q1 2025. Verify current capabilities with each vendor before procurement decisions.
Proven Accuracy
92% Detection Accuracy Validated Against Drilling Results
iFactory AI models trained on thousands of wells demonstrate superior fault detection, DHI validation, and reservoir prediction versus traditional interpretation methods, backed by drilling confirmations.
35%
More Prospects
2.5x
Better Discovery Rate
Regional Exploration Standards Compliance
iFactory AI seismic interpretation maintains compliance with upstream regulatory requirements across global oil and gas jurisdictions, supporting license applications and drilling permit submissions with technically defensible geological analysis.
Scroll to see full table
| Region | Seismic Standards | Exploration Compliance | Data Management |
|---|---|---|---|
| United States | SEG technical standards, PSDM imaging requirements for deepwater, Gulf of Mexico, BLM minimum data quality specifications | Well of control documentation per API standards, geological & geophysical reports for federal lease sales, environmental assessments for seismic surveys | Data retention per BOEM regulations, seismic metadata standards, well log archival to state geological surveys, digital submission formats |
| United Arab Emirates | ADNOC technical specifications for offshore seismic, Supreme Petroleum Council standards, international E&P best practices | Exploration license technical work programs, annual progress reports to regulatory authority, development plan submissions with seismic support | ADNOC data management requirements, seismic data relinquishment upon license expiry, technical data rooms for bid rounds, Arabic language reporting |
| United Kingdom | OGA minimum seismic data standards, North Sea environmental survey requirements, UKCS technical guidelines | Well proposal geological prognosis, competent person sign-off for reserve estimates, Oil & Gas Authority reporting obligations | National Data Repository submissions, NSTA digital data standards, seismic reprocessing documentation, open access data release schedules |
| Canada | CAGC seismic acquisition standards, offshore East Coast regulatory requirements, Arctic survey environmental protocols | NEB/CER geological evaluations for significant discoveries, provincial resource assessments, indigenous consultation for exploration activities | Provincial geological survey data submissions, federal data repository compliance, seismic line metadata standards, bilingual technical documentation |
| Europe (EU) | OSPAR environmental impact assessments for marine seismic, EU Marine Strategy Framework compliance, national authority specifications | Hydrocarbon licensing authority technical requirements, environmental impact statements, cross-border coordination for transboundary reservoirs | Member state data management regulations, seismic survey notifications, exploration well reporting to national authorities, GDPR compliance for operational data |
iFactory provides documentation templates and compliance support for regulatory submissions. Contact support for region-specific exploration requirements.
Implementation Roadmap
AI seismic interpretation deployment follows phased approach from data preparation through model training to production analysis. Typical timeline: 8 to 12 weeks from project initiation to first interpretation results.
Week 1-2
Data Assembly & Quality Assessment
Client provides seismic datasets and well data for target basin. iFactory performs quality control: seismic navigation accuracy, processing artifacts identification, amplitude preservation verification. Well log quality assessment: missing intervals identified, log editing for spikes and bad data. Seismic-to-well ties established at all wells using synthetic seismograms from sonic and density logs. Data gaps documented, additional data requirements identified for optimal model training.
Week 3-5
Model Training & Validation
Training dataset compiled from wells with known outcomes: discoveries labeled as positive examples, dry holes as negative examples. Features extracted from seismic at well locations: amplitude, frequency, phase, 40+ attributes. Machine learning models trained for fault detection, horizon tracking, DHI classification, reservoir property prediction. Cross-validation performed on held-out wells. Model performance evaluated: fault detection 94%, DHI precision 82%, porosity RMSE 1.8 porosity units. Models approved for production deployment.
Week 6-8
Full Volume Interpretation Execution
Complete 3D seismic survey processed through AI pipeline: automated horizon picking across 24 stratigraphic levels, fault network detection identifying 187 faults, seismic attribute computation (instantaneous frequency, envelope, coherence, curvature), DHI detection flagging 47 amplitude anomalies, seismic inversion for reservoir property prediction. Interpretation completed in 72 hours of processing time. Results QC'd by iFactory geoscientists: validation of horizon correlations to wells, fault throw measurements verified, DHI classifications reviewed for geological plausibility.
Week 9-12
Prospect Generation & Delivery
AI integrates structural interpretation, DHI detections, reservoir predictions to identify drillable prospects. Each prospect evaluated for geological risk factors, probability of success calculated. Prospect inventory ranked by expected value considering reserve potential and drilling costs. Deliverables package prepared: interpretation database with horizons and faults, prospect maps showing closure and predicted reservoir quality, DHI analysis with confidence scores, drilling recommendations prioritized by POS. Client workshop conducted to review results, discuss geological interpretations, finalize drilling strategy. System transitioned to client for ongoing use.
Measured Results from Exploration Programs
92%
Detection Accuracy vs Drilling
35%
More Prospects Identified
78%
Faster Interpretation Time
2.5x
Higher Discovery Rate
58%
Dry Hole Risk Reduction
72hrs
Complete Interpretation Time
From the Field
"We acquired a new 3D seismic survey over our Gulf Coast exploration acreage and needed rapid interpretation to meet drilling decision timeline for upcoming rig availability. Traditional interpretation would require 4-5 months which exceeded our deadline. iFactory completed full interpretation in 10 weeks including model training: picked 18 horizons, identified fault network with 140+ faults, detected 23 DHI anomalies. Their AI flagged a stratigraphic trap we had completely missed in our initial manual review of 2D lines, amplitude anomaly at channel pinchout barely visible in conventional display but clearly identified by pattern recognition algorithm. We drilled the AI-recommended location, discovered 8 MMboe oil accumulation that would have been bypassed with traditional interpretation. The discovery paid for the AI interpretation service 40 times over. Now using AI as standard workflow for all new seismic, our exploration success rate improved from 22% to 38% in past 18 months by drilling higher-quality AI-ranked prospects."
VP Exploration
Independent E&P Company, US Gulf Coast Operations, 120 MMboe Reserves
Frequently Asked Questions
QHow much well control data is required to train AI models for a new basin?
Minimum 15-20 wells with complete log suites (sonic, density, resistivity, gamma ray) tied to seismic data provide sufficient training for initial models. More wells improve accuracy, but transfer learning from global models trained on thousands of wells enables deployment in data-sparse basins. Models continuously improve as new wells drilled and added to training dataset. Discuss data requirements for your exploration area.
QCan AI interpretation handle complex geological settings like salt domes or overthrust belts?
Yes, AI trained on complex structural examples learns to interpret challenging geology. Salt dome imaging requires specialized seismic processing (PSDM) as input, then AI detects sub-salt prospects and maps salt flanks. Overthrust interpretation benefits from fault detection algorithms identifying thrust surfaces. Model performance depends on training data representing target geological complexity, specialized model architectures handle specific challenges like velocity pull-up or structural shadows.
QHow do you validate AI interpretation accuracy before committing to expensive drilling decisions?
Validation uses blind well testing: models trained on subset of wells, tested on remaining wells never seen during training. Predictions at blind well locations compared to actual drilling results to measure accuracy. Industry standard validation shows AI fault detection 94%, DHI classification 82% precision, reservoir property prediction within 15% of measured values. Additional confidence from model uncertainty quantification highlighting predictions requiring geoscience review before drilling.
QDoes AI replace geophysicists or serve as interpretation tool for technical teams?
AI augments geophysicist capabilities rather than replacing expertise. System handles time-consuming tasks like horizon picking and attribute computation, freeing geoscientists for higher-value geological analysis and drilling recommendations. Final prospect decisions combine AI analysis with geoscience judgment, analog comparisons, and economic evaluation. Human-in-the-loop workflow where geophysicists review AI outputs and provide feedback creates collaborative interpretation superior to either approach alone. Exploration teams using AI report productivity gains while maintaining technical oversight.
Continue Reading
Predictive Maintenance for Drilling EquipmentAI monitoring that complements seismic-guided drilling by preventing equipment failures during exploration well operations.
Read more
AI Reservoir OptimizationProduction optimization using reservoir models constrained by seismic interpretation and drilling results.
Read more
Drilling Automation with AIAutomated drilling systems executing exploration well plans generated from AI seismic interpretation.
Read more
AI Well Log AnalysisAutomated log interpretation providing ground truth data for training and validating AI seismic models.
Read more
Interpret Seismic Data 78% Faster with 92% Detection Accuracy Using AI
iFactory's machine learning platform automates seismic interpretation from horizon picking through prospect ranking, delivering drill-ready exploration targets in 72 hours with accuracy validated against thousands of drilling results across global basins.
Automated Horizon Picking
AI Fault Detection
DHI Validation
Reservoir Prediction
Prospect Ranking
