--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - oil-and-gas - upstream - production-engineering - decline-curve-analysis - arps-decline - artificial-lift - reservoir-engineering - workover-prediction - water-breakthrough - xpertsystems pretty_name: "OIL-013 — Synthetic Production Time-Series Dataset (Sample)" size_categories: - 100K q(t) = qi / (1 + b·di·t)^(1/b) — Arps hyperbolic > oil_rate = q(t) × seasonal × noise × uptime × lift_factor The **decline-curve fidelity is high**: at sample scale, the mean absolute relative error between actual rates and pure-Arps predictions on anomaly- free days is ~6%, with the residual driven by lift degradation (built-in) and operational noise (1.5% std). The full Arps physics is preserved well-by-well — see the scorecard for the explicit fidelity check. **`reservoir_pressure.csv`** — biweekly pressure tests with realistic drawdown modeling: > reservoir_pressure(d) = p0 × (1 − 0.22 × d/sim_days) + N(0, 45) > bhfp = reservoir_pressure − U(250, 1700) > drawdown = reservoir_pressure − bhfp Pressure-test quality follows the **A/B/C grading convention** common in production engineering (40% A, 40% B, 20% C). **`artificial_lift.csv`** — weekly performance per lift type. ESP wells get full instrumentation (ESP frequency Hz, pump efficiency, motor temperature F, vibration index); rod pump / PCP / gas lift / plunger get pump efficiency + vibration only. **ESP frequency centered at 52 Hz** per API/SPE 174021 ESP operating-range guidance. **`production_labels.csv`** — monthly ML labels with **6-class forecast classification**: | Class | Trigger | |---|---| | `stable` | oil_rate ≥ 0.60 × qi AND wc ≤ 62% | | `moderate_decline` | oil_rate < 0.60 × qi | | `workover_candidate` | oil_rate < 0.35 × qi OR wc > 62% | | `water_breakthrough` | wc > 75% | | `lift_limited` | non-natural-flow AND uptime < 78% | | `steep_decline` | oil_rate < 0.20 × qi at early time | Plus four binary flags: `workover_candidate_flag`, `water_breakthrough_flag`, `steep_decline_flag`, `lift_limited_flag`. **`production_allocations.csv`** — per-well multi-zone allocation using **Dirichlet sampling** over 1-4 zones; per-well percentages sum to exactly 100%. Allocation methods follow standard production engineering practice: test separator / production logging / model based / commingled estimate (weighted equally). **`flow_assurance_events.csv`** — 6-class flow assurance taxonomy aligned with **NACE corrosion standards + SPE flow assurance literature**: scale / paraffin / hydrate / sand / emulsion / corrosion. Per-event domain-specific risk indices. --- ## Suggested use cases 1. **Arps decline curve regression** — fit hyperbolic Arps parameters (qi, di, b) from the first 60-180 days of daily production for each well; benchmark against the ground-truth params in `wells_master.csv`. Strong physics signal — sample mean Arps fidelity is ~94%. 2. **6-class forecast class classification** — multi-class classifier on `forecast_class` from daily production + lift + pressure features. 3. **Workover candidate prediction** — binary classifier on `workover_candidate_flag` from upstream features. Highly class-imbalanced (~3% positives), realistic for production engineering operations. 4. **Water breakthrough prediction** — binary or time-to-event modeling on `water_breakthrough_flag` from water-cut trajectory features. 5. **ESP failure prediction** — train RUL or binary failure classifier on `artificial_lift.csv` ESP-only rows using vibration, motor temperature, pump efficiency degradation as features. 6. **Multi-zone allocation regression** — predict per-zone allocation percentages from well characteristics and zone metadata. 7. **Flow assurance type classification** — 6-class classifier on `flow_assurance_type` from well characteristics and production conditions. 8. **Production rate forecasting** — N-day-ahead time-series forecasting of oil/gas/water rates from historical features (LSTM / Transformer / TFT benchmark target). 9. **Downtime root-cause classification** — 6-class classifier on `root_cause_category` (surface/subsurface/facility/weather/power/ unknown) from production anomaly patterns. 10. **Multi-table relational ML** — entity-resolution and graph neural-network learning across the 11 joinable tables via `well_id` + `production_date`. --- ## Loading ```python from datasets import load_dataset ds = load_dataset("xpertsystems/oil013-sample", data_files="daily_production.csv") print(ds["train"][0]) ``` Or with pandas: ```python import pandas as pd wells = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/wells_master.csv") daily = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/daily_production.csv") lift = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/artificial_lift.csv") labels = pd.read_csv("hf://datasets/xpertsystems/oil013-sample/production_labels.csv") # Join daily production to wells master for asset-type / completion-type features joined = daily.merge(wells, on="well_id") # Join labels to daily production (monthly labels propagated to all days in month) labels["label_date"] = pd.to_datetime(labels["label_date"]) daily["production_date"] = pd.to_datetime(daily["production_date"]) ``` --- ## Reproducibility All generation is deterministic via the integer `seed` parameter (driving both `random.seed` and `np.random.default_rng`). A seed sweep across `[42, 7, 123, 2024, 99, 1]` confirms Grade A+ on every seed in this sample. --- ## Honest disclosure of sample-scale limitations This is a **sample** product calibrated for production-engineering and decline-curve ML research, not for live production-allocation decisions. A few notes: 1. **Initial rates run higher than declared base parameter.** The generator's `--mean-initial-oil-rate-bopd` parameter is 950 BOPD, but the actual observed mean is ~1100 BOPD because **two stacked lognormal multipliers** (basin profile `qi_mult` × per-well `lognormal(0, 0.25)`) compound to a multiplier mean > 1. Same pattern for GOR (declared 1450, observed ~1800). This is realistic (real production distributions have positive skew), but if you need a pure declared-target match, scale `--mean-initial-oil-rate- bopd` down by ~13% to compensate for the lognormal-compound bias. 2. **Anomaly and downtime injection rates are very low.** The generator divides `anomaly_injection_rate / 365.25` and `downtime_event_rate / 365.25` to convert per-year rates to per-day probabilities. At sample defaults (3% per year, 2.2% per year), this gives ~0.0001 daily probability — essentially zero anomalies in the daily timeseries (~0.01% rate observed). The `downtime_events.csv` table is separately generated via a Poisson model and *is* populated (~2 events/well), so downtime ML training uses that table, not the per-day anomaly flag. 3. **Forecast class distribution is heavily skewed toward "stable" (~97%)** because the simulation runs only 365 days — Arps hyperbolic decline with mean b=1.0 and di=0.23 produces only ~20-27% rate decline in year 1, which keeps most wells in the "stable" class (oil_rate ≥ 0.60 × qi). For long-horizon forecast ML, use the full product with `--simulation-days 1800+` to see meaningful class diversity (steep_decline, lift_limited, water_breakthrough all populate substantially over 3-5 years). 4. **Stimulation events are extremely sparse** (~1 event in 250 wells at sample scale) because the generator uses a one-time Bernoulli draw per well with combined probability ~2.4%. Full product (120K wells × 3650 days) gives ~3000 stimulation events with full event-type diversity. For workover ML at sample scale, use the `workover_candidate_flag` in `production_labels.csv` (synthesized from production patterns) rather than the literal stimulation_events table. 5. **Mass balance is exact (>99.99%)** because the generator's `cumulative_oil_bbl` column is computed as a running sum of `oil_rate_bopd`. This is a property of the simulation, not a physics test — but it does confirm proper integration. Use it as sanity check, not as evidence of advanced reservoir physics. 6. **Pressure decline is linear**, not exponential. The generator uses `p(d) = p0 × (1 - 0.22 × d/sim_days)`, which is a simple linear depletion model. Real reservoirs follow material-balance- driven decline with B-factor and aquifer support — for reservoir-engineering-grade decline modeling, use SPE-PEH-Vol-V compliant tools rather than the OIL-013 pressure column. 7. **Allocation methods are uniformly weighted**, not conditioned on well type or facility. Real production allocations heavily favor test-separator for low-rate wells, model-based for commingled pads, and production-logging for problem wells. Future generator v1.1 will introduce conditioning. --- ## Full product The **full OIL-013 dataset** ships at **120,000 wells × 3,650 days** (prod mode) producing several hundred million daily production rows with substantial populated stimulation/workover events, full multi-year decline curves enabling meaningful forecast-class diversity, and basin-conditioned operator behavior priors — licensed commercially. Contact XpertSystems.ai for licensing terms. 📧 **pradeep@xpertsystems.ai** 🌐 **https://xpertsystems.ai** --- ## Citation ```bibtex @dataset{xpertsystems_oil013_sample_2026, title = {OIL-013: Synthetic Production Time-Series Dataset (Sample)}, author = {XpertSystems.ai}, year = {2026}, url = {https://huggingface.co/datasets/xpertsystems/oil013-sample} } ``` ## Generation details - Sample version : 1.0.0 - Random seed : 42 - Generated : 2026-05-22 12:39:33 UTC - Wells : 250 - Simulation days : 365 - Basins : 10 (Permian, Eagle Ford, Bakken, Marcellus, Haynesville, GoM, North Sea, Middle East, W Canada, Brazil Pre-Salt) - Asset types : 8 (unconventional shale oil, tight oil, shale gas, deepwater, offshore sandstone, carbonate, heavy oil, deepwater carbonate) - Completion types : 6 (horizontal multistage frac, vertical, deviated, multilateral, open hole, cased hole) - Lift types : 6 (natural flow, ESP, rod pump, gas lift, PCP, plunger lift) - Downtime types : 8 (planned, unplanned, facility, weather, flow assurance, lift, integrity, power) - Flow assurance : 6 (scale, paraffin, hydrate, sand, emulsion, corrosion) - Forecast classes : 6 (stable, moderate decline, steep decline, water breakthrough, lift limited, workover candidate) - Calibration basis : Arps (1945), SPE PEH Vol V, SPE 152596, SPE 167242, SPE 174021, API RP-11ER, EIA AEO, Rystad ShaleWellCube, IHS Markit, NACE, IOGP allocation - Overall validation: 100.0/100 — Grade A+