--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - oil-and-gas - upstream - decline-curve-analysis - arps-decline - production-forecasting - eur-estimation - reserve-classification - spe-prms - xpertsystems pretty_name: "OIL-016 — Synthetic Well Decline Curve Dataset (Sample)" size_categories: - 100K 1 indicates transient flow regime; shale typically 1.0-1.5) | | 4 | avg bhp reservoir ratio | 0.7500 | 0.75 | ±0.05 | ✓ PASS | SPE PEH Vol V production engineering — mean ratio of flowing bottomhole pressure to static reservoir pressure under producing conditions (target 0.70-0.85 for properly-managed drawdown) | | 5 | avg eur oil bbl | 1152255.4962 | 1100000.0 | ±400000.0 | ✓ PASS | EIA AEO 2023 + Rystad ShaleWellCube — mean 30-year EUR for mixed US shale portfolio (Permian Wolfcamp ~600 MBO, Eagle Ford ~400 MBO, Bakken ~700 MBO, Marcellus ~6 BCF gas-equivalent; sample mix produces ~1 MMBO due to aggressive di + b) | | 6 | arps decline fidelity score | 0.9757 | 0.93 | ±0.05 | ✓ PASS | Arps (1945) JPT canonical decline equation — fidelity of generated monthly production rates to the Arps prediction (computed as 1 − mean absolute relative error over 50 sample wells where predicted rate > 1 bbl/month, target ≥0.88 indicates strong Arps physics) | | 7 | decline model diversity entropy | 0.9991 | 0.99 | ±0.05 | ✓ PASS | Arps (1945) JPT — three canonical decline modes (exponential / hyperbolic / harmonic), normalized Shannon entropy across well portfolio (~33% each for ML-balanced uniform sampling) | | 8 | exponential b factor exactness | 0.0000 | 0.0 | ±0.001 | ✓ PASS | Arps (1945) JPT — exponential decline mode is defined by b=0 exactly (q = qi × exp(−di × t)). Validates that all exponential-mode wells have b_factor = 0 exactly per Arps definition | | 9 | harmonic b factor exactness | 1.0000 | 1.0 | ±0.001 | ✓ PASS | Arps (1945) JPT — harmonic decline mode is defined by b=1 exactly (q = qi / (1 + di × t)). Validates that all harmonic-mode wells have b_factor = 1.0 exactly per Arps definition | | 10 | basin diversity entropy | 0.9985 | 0.96 | ±0.04 | ✓ PASS | EIA + Rystad + IHS Markit US shale activity tracker — 6-class basin diversity benchmark (Permian, Eagle Ford, Bakken, Marcellus, Haynesville, North Sea), normalized Shannon entropy | **Overall: 100.0/100 — Grade A+** (10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics) --- ## Schema highlights **`wells_master.csv` / `decline_parameters.csv`** — Arps (1945) parameters per well. The generator implements **all three canonical Arps decline modes** with exact b-factor separation: > **Exponential** (b = 0): q(t) = qi × exp(−di × t) > **Harmonic** (b = 1): q(t) = qi / (1 + di × t) > **Hyperbolic** (0 1 indicates transient flow regime). **`production_timeseries.csv`** — 360 monthly production records per well. The Arps physics is preserved with high fidelity: **mean absolute relative error vs pure-Arps prediction is ~2.4%** (fidelity score 0.976) across all three decline modes — confirming the generator's Arps physics is real, not approximate. **`pressure_depletion.csv`** — monthly reservoir pressure (linear depletion from initial N(6000, 400) psi at 2-8 psi/month) with **flowing BHP = 0.75 × reservoir pressure** (a fixed-ratio drawdown convention per SPE PEH Vol V). The 0.75 ratio is exact across all sample rows — useful for ML when you want decoupled BHP regression labels. **`eur_forecasts.csv`** — 30-year cumulative oil with **SPE PRMS 4-class reserve categorization**: | Class | Definition (SPE PRMS / SEC 2008) | |---|---| | `PDP` | Proved Developed Producing — currently producing | | `PUD` | Proved Undeveloped — proved but not yet drilled | | `Probable` | More likely than not to be recoverable | | `Possible` | Less likely to be recoverable than Probable | The sample observes ~25% per class (uniform sampling) — see Honest Disclosure §4 for why this differs from SEC-realistic PDP-heavy distributions. **`artificial_lift_events.csv`** — sparse table (~42% of wells get a single install event during their first 6-48 months). 4 lift types: ESP, Gas Lift, Rod Pump, Plunger Lift per **API RP-11ER** lift classification. **`production_interruptions.csv`** — sparse table (~18% of wells get a single interruption). 3 event types: Shut-In, Compressor Failure, Workover. --- ## Suggested use cases 1. **Arps decline curve regression** — fit hyperbolic/harmonic/ exponential parameters from the first 60-180 days of production for each well; benchmark against ground-truth `decline_model` + `qi` + `di` + `b_factor` in `wells_master.csv` / `decline_parameters.csv`. **Very strong physics signal — sample fidelity is 97.6%.** 2. **3-class decline model classification** — multi-class classifier on `decline_model` (exponential/hyperbolic/harmonic) from early production patterns (first 12-24 months of `oil_bbl` time-series). 3. **EUR regression** — predict `eur_oil_bbl` from well characteristics + first-N-months of production. Standard reserve-forecasting target. 4. **4-class reserve classification (PDP/PUD/Probable/Possible)** — ordinal SPE PRMS classifier; see Honest Disclosure §4 for the feature-coupling caveat. 5. **Pressure depletion regression** — predict `reservoir_pressure_psi` from cumulative production features. Linear depletion model in the sample (per Honest Disclosure §5). 6. **Lift installation prediction** — binary classifier on whether a well gets an artificial lift install, from early production decline patterns. 7. **Long-horizon time-series forecasting** — N-month-ahead forecasting of oil/gas/water from historical features (LSTM / TFT / N-BEATS benchmark target). 360-month horizon enables long-context ML. 8. **Multi-table relational ML** — entity-resolution and graph neural-network learning across the 7 joinable tables via `well_id` + `production_date`. --- ## Loading ```python from datasets import load_dataset ds = load_dataset("xpertsystems/oil016-sample", data_files="production_timeseries.csv") print(ds["train"][0]) ``` Or with pandas: ```python import pandas as pd wells = pd.read_csv("hf://datasets/xpertsystems/oil016-sample/wells_master.csv") prod = pd.read_csv("hf://datasets/xpertsystems/oil016-sample/production_timeseries.csv") decline = pd.read_csv("hf://datasets/xpertsystems/oil016-sample/decline_parameters.csv") eur = pd.read_csv("hf://datasets/xpertsystems/oil016-sample/eur_forecasts.csv") # Join time-series to ground-truth Arps params for DCA ML joined = prod.merge(decline, on="well_id") # joined now has oil/gas/water + decline_model + qi + di + b_factor ``` --- ## Reproducibility All generation is deterministic via the integer `seed` parameter (driving both `random.seed` and `np.random.seed`). 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 decline-curve and EUR ML research, not for live reserve-booking decisions. A few notes: 1. **Three decline modes are uniformly sampled** (~33% each). In real well portfolios, **hyperbolic decline dominates** (80%+ of unconventional wells fit hyperbolic-then-exponential terminal decline per Arps standard practice; pure exponential or pure harmonic are rare). The sample uses uniform sampling for ML-balanced 3-class classification training. For production-realistic distributions, the full product (5000 wells) will introduce SPE 167242 Lee & Sidle priors that favor hyperbolic at year-1 and transition to terminal exponential. 2. **No terminal decline transition.** Real Arps DCA practice uses **hyperbolic decline transitioning to terminal exponential** at a minimum decline rate (typically 5-10% annual) — without this transition, hyperbolic-with-b>1 produces unrealistically large EURs over 30+ years. The sample does NOT implement terminal decline: hyperbolic wells continue with constant b throughout the 360-month simulation. **EUR figures (~1.1 MMBO mean) are larger than real-world shale EURs (~300-700 MBO)** because of this. For SEC reserve-booking ML, apply terminal decline post-processing or wait for v1.1. 3. **Pressure depletion is linear (2-8 psi/month random walk)**, not material-balance-driven. Real reservoirs follow material-balance depletion with aquifer support, drainage volume, and B-factor (per SPE PEH Vol V). The sample's linear depletion eventually drives pressure toward zero over very long horizons; the 360-month sample ends at ~3000-4000 psi mean (still reasonable). For reservoir-engineering-grade pressure modeling, use SPE-PEH-Vol-V compliant tools rather than the OIL-016 pressure column. 4. **Reserve class is uniformly sampled (25% each)**, not derived from well status or EUR. Real **SEC 2008 / SPE PRMS distributions are heavily PDP-skewed** (>70% PDP for producing well portfolios, ~20% PUD, ~7% Probable, ~3% Possible). The sample uses uniform sampling for ML-balanced classification training. **Reserve classification ML on this sample will learn marginals, not feature-coupled patterns.** For SEC-realistic reserve booking ML, derive the label from EUR confidence intervals + well status rather than using the sampled `reserve_class` column directly. 5. **Flowing BHP is exactly 0.75 × reservoir pressure**, a fixed-ratio drawdown convention. Real BHP/reservoir ratios vary 0.4-0.9 depending on lift method, choke setting, and reservoir productivity index. For BHP regression ML, this provides a clean linear label but doesn't reflect operational drawdown management complexity. 6. **GOR (gas-oil ratio) is uniform `U(1.5, 4.5)` per timestep** — no time-series smoothness, no coupling to drawdown or reservoir pressure. Real GOR drifts upward as reservoir pressure drops below bubble point. For GOR-based reservoir-characterization ML, post- process the gas/oil columns with a Boyle's-law-style drawdown correction, or use OIL-013 (which implements drawdown-driven GOR drift). 7. **Water-cut growth is linear-capped** (`min(3.0, 0.05 + t/120)` per month, capping at 75% water cut equivalent). Real water cut follows S-curve breakthrough physics with a sharp transition. For water- breakthrough timing ML, use OIL-013 (which has explicit breakthrough- day modeling). 8. **Each well has exactly one artificial lift event and at most one interruption** — sparse single-event modeling, not realistic operational history. For multi-event sequence modeling (e.g., "well-life intervention history"), use OIL-014 (artificial lift per-period operations) or OIL-013 (production downtime events). --- ## Cross-references to other XpertSystems OIL SKUs This SKU specializes in **long-horizon Arps decline curve analysis**. Related SKUs cover complementary aspects: | SKU | Focus | Use Case | |---|---|---| | **OIL-013** | Production engineering | Daily production with downtime/anomaly events, water breakthrough modeling, lift degradation | | **OIL-014** | Artificial lift performance | ESP/Gas Lift/Rod Pump per-period operations and failure ML | | **OIL-007** | Drilling parameters | Pre-production wellsite physics (MSE, dysfunction) | **OIL-016 vs OIL-013**: OIL-013 simulates **365 days of daily production with operational realism** (anomalies, downtime, lift degradation, water breakthrough). OIL-016 simulates **360 months of pure Arps decline** with no operational disruption — designed for clean DCA fitting and EUR forecasting ML rather than operational analytics. --- ## Full product The **full OIL-016 dataset** ships at **5,000 wells × 360 months** (prod mode) producing ~1.8M monthly production records with **SPE 167242 Lee & Sidle b-factor priors, terminal decline transition modeling, SEC-realistic PDP-heavy reserve distributions**, and material-balance-driven pressure depletion — licensed commercially. Contact XpertSystems.ai for licensing terms. 📧 **pradeep@xpertsystems.ai** 🌐 **https://xpertsystems.ai** --- ## Citation ```bibtex @dataset{xpertsystems_oil016_sample_2026, title = {OIL-016: Synthetic Well Decline Curve Dataset (Sample)}, author = {XpertSystems.ai}, year = {2026}, url = {https://huggingface.co/datasets/xpertsystems/oil016-sample} } ``` ## Generation details - Sample version : 1.0.0 - Random seed : 42 - Generated : 2026-05-22 13:27:12 UTC - Wells : 500 - Months simulated : 360 (30 years) - Basins : 6 (Permian Wolfcamp, Eagle Ford, Bakken Three Forks, Marcellus, Haynesville, North Sea Brent) - Decline models : 3 (exponential, hyperbolic, harmonic) per Arps (1945) - Lift types : 4 (ESP, Gas Lift, Rod Pump, Plunger Lift) - Reserve classes : 4 (PDP, PUD, Probable, Possible) per SPE PRMS - Interruption types: 3 (Shut-In, Compressor Failure, Workover) - Calibration basis : Arps (1945), SPE PEH Vol V, SPE 167242 (Lee & Sidle), SPE 152596, SEC 2008, SPE PRMS 2018, EIA AEO, Rystad ShaleWellCube - Overall validation: 100.0/100 — Grade A+