license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- seismic
- geophysics
- oil-and-gas
- exploration
- reservoir-engineering
- avo-analysis
- reservoir-characterization
- dhi
- subsurface
- synthetic-data
pretty_name: OIL-001 — Synthetic Seismic Survey Dataset (Sample)
size_categories:
- 10K<n<100K
OIL-001 — Synthetic Seismic Survey Dataset (Sample)
XpertSystems.ai Synthetic Data Platform · SKU: OIL001-SAMPLE · Version 1.0.0
This is a free preview of the full OIL-001 — Synthetic Seismic Survey Dataset product. It contains roughly ~25% of the full dataset at identical schema, physics modeling, and seismic interpretation features, so you can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|---|---|---|---|
seismic_traces.csv |
~28,800 | ~100,000 | Per-trace amplitude + attributes (25 cols) |
horizon_catalog.csv |
~5,850 | ~30,000 | Interpreted horizons with rock physics (27 cols) |
survey_geometry.csv |
~3 | ~6 | Per-survey geometry & acquisition (23 cols) |
interpretation_summary.csv |
~3 | ~6 | Per-survey interpretation KPIs (18 cols) |
Dataset Summary
OIL-001 is a full seismic simulation engine producing realistic 2D/3D seismic waveforms with subsurface structure interpretation labels — the kind of data exploration geophysicists, reservoir engineers, and seismic AI companies (Bluware, Earth Science Analytics, OspreyData, Geoteric) build their models on, but synthetic and free under CC-BY-NC for research.
Physics modeling:
- Convolutional seismic model: reflectivity series × wavelet (Ricker or zero-phase)
- Aki-Richards approximation: angle-dependent reflection coefficients for AVO analysis
- Gassmann fluid substitution: mineral/dry-frame/fluid moduli per layer
- Gardner's relation: density from Vp by lithology
- NMO (Normal Moveout): velocity-based gather flattening for CDP stack
- Hilbert transform: instantaneous amplitude, phase, frequency attributes
- Negative-pressure coherence: fault-zone similarity attribute
Survey acquisition modeling:
- 3D land, 3D marine, 2D 2D-towed-streamer survey types
- Configurable inline/crossline grid spacing
- 9 offset panels (0°-40° in 5° steps) for AVO gathers
- CDP fold, sample interval (2 ms = 500 Hz), 4-second TWT record length
Subsurface structure model (8 structure labels):
- Anticline crest / Anticline flank / Syncline
- Fault plane / Fault shadow
- Salt flank
- Stratigraphic trap
- Flat background
DHI (Direct Hydrocarbon Indicator) injection (6 DHI labels):
- No DHI (most traces)
- Bright spot (amplitude anomaly indicating gas)
- Dim spot (amplitude reduction indicating oil)
- Flat spot (fluid contact reflection)
- Polarity reversal (Class IIp AVO response)
- AVO anomaly (Class III gas response)
8 lithology types:
sandstone (reservoir), shale (seal), limestone, dolomite, salt, anhydrite, volcanic, basement
5 fluid types (with Gassmann fluid substitution):
brine, oil, gas, gas_condensate, tight_dry
Seismic noise injection (7 noise types):
- Surface multiple reflections
- Interbed multiple reflections
- Ambient random noise
- Source interference
- Coupling variation
- Statics anomaly
- Clean (no noise)
Per-trace seismic attributes (industry-standard interpretation features):
- Raw and processed amplitudes
- Instantaneous frequency, phase, amplitude (Hilbert transform)
- Envelope (dB)
- Coherence score (fault-zone similarity)
- Curvature (k1 principal curvature)
- AVO intercept and gradient (Aki-Richards)
- Noise type and flag
Per-horizon rock physics:
- Vp, Vs, Vp/Vs ratio
- Density (g/cc, Gardner-derived)
- Acoustic impedance
- Porosity %, water saturation %
- Net pay, dip angle, dip azimuth
Prospect risking (per-horizon):
- PGOS (Probability of Geological Success) — typical industry 0.20-0.40
- Trap closure area (km²)
- Reservoir quality class
- Fault association
Calibrated Validation Results
Sample validation results across 10 seismic-domain KPIs:
| Metric | Observed | Target | Source | Verdict |
|---|---|---|---|---|
| n_surveys_represented | 3 | 3 | Sample survey count | ✓ PASS |
| n_survey_types | 2 | 2 | 2D + 3D + 4D coverage | ✓ PASS |
| n_basins_represented | 3 | 3 | Geographic diversity | ✓ PASS |
| dominant_frequency_hz | 37.00 | 38.00 | Standard seismic dominant freq | ✓ PASS |
| snr_db_mean | 21.03 | 22.00 | Industry SNR target (post-stack) | ✓ PASS |
| horizon_pick_confidence | 0.861 | 0.880 | SEG interpreter confidence | ✓ PASS |
| horizon_continuity_index | 0.868 | 0.830 | Lateral continuity benchmark | ✓ PASS |
| fault_detection_confidence | 0.813 | 0.790 | Coherence-based fault picking | ✓ PASS |
| sandstone_porosity_pct_mean | 16.93 | 17.50 | Industry sandstone reservoir φ | ✓ PASS |
| n_horizon_labels_represented | 8 | 3 | Multi-class horizon diversity | ✓ PASS |
Note: This dataset is designed for seismic interpretation AI training — buyers building horizon-picking auto-trackers, fault-detection neural networks, DHI classification models, or AVO inversion solvers can use these labels for supervised training. The full product includes 6 surveys with larger 3D grids and full structural diversity.
Schema Highlights
seismic_traces.csv (primary file, 25 columns)
Trace identification & geometry:
| Column | Type | Description |
|---|---|---|
| survey_id, gather_id | int | Survey and CDP gather IDs |
| cdp_x, cdp_y | float | CDP location (meters) |
| cdp_inline, cdp_crossline | int | 3D grid coordinates |
| offset_m | float | Source-receiver offset (m) |
| two_way_time_ms | float | Two-way time (ms) |
Amplitudes & attributes:
| Column | Type | Description |
|---|---|---|
| amplitude_raw | float | Pre-processing amplitude |
| amplitude_processed | float | After deconv + statics + multiples |
| frequency_hz | float | Instantaneous frequency |
| instantaneous_phase_deg | float | Hilbert phase (degrees) |
| instantaneous_amplitude | float | Hilbert envelope |
| envelope_db | float | Envelope in dB |
| coherence_score | float | Coherence (0-1, fault zones low) |
| curvature_k1 | float | Principal curvature |
| avo_intercept, avo_gradient | float | Aki-Richards AVO A, B |
Labels:
| Column | Type | Description |
|---|---|---|
| horizon_label | string | 8 horizon labels (basement, reservoir, etc.) |
| structure_label | string | 8 structure labels (anticline, fault, salt) |
| dhi_label | string | 6 DHI labels |
| noise_flag, noise_type | string | Noise contamination flag |
| fault_proximity_m | float | Distance to nearest fault |
| reservoir_quality_flag | string | Reservoir quality class |
horizon_catalog.csv (27 columns)
Per-horizon rock physics + structural interpretation:
| Column | Description |
|---|---|
| horizon_name, horizon_label | Named horizon + 8-class label |
| two_way_time_ms, depth_m | TWT and depth |
| dip_angle_deg, dip_azimuth | Structural dip |
| interval_velocity_ms | Interval Vp (m/s) |
| acoustic_impedance_mpa | Acoustic impedance |
| vp_ms, vs_ms | Vp, Vs (m/s) |
| density_gcc | Density (g/cc, Gardner) |
| porosity_pct | Porosity (%) |
| water_saturation_pct | Water saturation (%) |
| lithology_type | 8 lithology classes |
| fluid_type | 5 fluid types |
| trap_style | Trap classification |
| dhi_class, dhi_label | DHI class + 6-class label |
| reservoir_quality_class | Reservoir quality |
| net_pay_m | Net pay thickness |
| fault_id | Associated fault |
| prospect_pgos | Probability of Geological Success |
survey_geometry.csv (23 columns)
Per-survey acquisition geometry: survey_type, basin_name, n_inlines, n_crosslines, cdp_spacing, fold, source_type, water_depth, acquisition environment, processing vintage, migration type, SNR, dominant frequency.
interpretation_summary.csv (18 columns)
Per-survey interpretation KPIs: horizon_pick_confidence, fault_confidence, horizon_continuity_index, multiple_contamination_pct, etc.
Suggested Use Cases
- Auto horizon picking — train CNNs to follow horizons across 3D
- Fault detection — train coherence/curvature-based fault networks
- DHI classification — 6-class hydrocarbon indicator prediction
- AVO inversion — predict elastic properties from intercept/gradient
- Salt body segmentation — geometric salt detection
- Structural label segmentation — 8-class structural interpretation
- Lithology prediction — 8-class lithology from seismic attributes
- Fluid type prediction (with Gassmann substitution targets)
- Reservoir quality scoring from acoustic impedance and porosity
- Multiple suppression — deep-learning-based denoise training
- Statics correction — anomaly detection at trace level
- Wavelet estimation & deconvolution — Ricker/zero-phase fitting
- Velocity model building — interval velocity prediction from traces
- Time-to-depth conversion — TWT-depth modeling
- Prospect screening — PGOS prediction from seismic features
- Trap closure area estimation from structural attributes
- Insurtech-style geophysical AI training without proprietary survey data
- University geophysics curriculum — synthetic teaching corpus
Loading the Data
import pandas as pd
traces = pd.read_csv("seismic_traces.csv")
horizons = pd.read_csv("horizon_catalog.csv")
geometry = pd.read_csv("survey_geometry.csv")
interp = pd.read_csv("interpretation_summary.csv")
# Multi-class horizon label target (8 classes)
y_horizon = traces["horizon_label"]
# Multi-class structural interpretation target (8 classes)
y_structure = traces["structure_label"]
# Multi-class DHI prediction target (6 classes)
y_dhi = traces["dhi_label"]
# Binary noise contamination
y_noise = traces["noise_flag"]
# Regression: AVO inversion targets
y_avo_a = traces["avo_intercept"]
y_avo_b = traces["avo_gradient"]
# Multi-class lithology from horizon (8 classes)
y_lithology = horizons["lithology_type"]
# Multi-class fluid type from horizon (5 classes)
y_fluid = horizons["fluid_type"]
# Regression: reservoir porosity
y_porosity = horizons["porosity_pct"]
# Regression: PGOS prospect risking
y_pgos = horizons["prospect_pgos"]
# Build 3D seismic cube for ML
cube = traces.pivot_table(
index=["cdp_inline", "cdp_crossline"],
columns="two_way_time_ms",
values="amplitude_processed",
aggfunc="mean"
)
License
This sample is released under CC-BY-NC-4.0 (free for non-commercial research and evaluation). The full production dataset is licensed commercially — contact XpertSystems.ai for licensing terms.
Full Product
The full OIL-001 dataset includes 6 surveys with 120×80 3D grids each, full structural diversity (anticlines, synclines, salt diapirs, stratigraphic traps), 14-layer velocity models with Gassmann substitution, and full DHI/AVO anomaly catalogs. Calibrated to SEG seismic interpretation standards, Aki-Richards AVO theory, and Gassmann fluid substitution theory.
📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai
Citation
@dataset{xpertsystems_oil001_sample_2026,
title = {OIL-001: Synthetic Seismic Survey Dataset (Sample)},
author = {XpertSystems.ai},
year = {2026},
url = {https://huggingface.co/datasets/xpertsystems/oil001-sample}
}
Generation Details
- Generator version : 1.0.0
- Random seed : 42
- Generated : 2026-05-16 22:41:59 UTC
- Surveys : 3 × (40 inlines × 30 crosslines × 9 offsets × 14 layers)
- Wavelet : Ricker, 38 Hz dominant
- Physics : Aki-Richards AVO + Gassmann + Gardner + Hilbert
- Calibration basis : SEG seismic interpretation standards
- Overall validation: 100.0 / 100 (grade A+)