| ---
|
| 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<n<1M
|
| ---
|
|
|
| # OIL-016 — Synthetic Well Decline Curve Dataset (Sample)
|
|
|
| **SKU:** `OIL016-SAMPLE` · **Vertical:** Oil & Gas / Upstream Reservoir Engineering
|
| **License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil016.v1`
|
| **Sample version:** `1.0.0` · **Default seed:** `42`
|
|
|
| A free, schema-identical preview of XpertSystems.ai's enterprise well decline-
|
| curve dataset for Arps DCA ML, EUR forecasting, reserve classification, and
|
| long-horizon production analytics. The sample covers **500 wells**
|
| across **6 global basins**, simulated over **360 months
|
| (30 years)**, with **361,809 rows** including **180,000 monthly
|
| production records** linked across **7 tables**.
|
|
|
| ---
|
|
|
| ## What's in the box
|
|
|
| | File | Rows | Cols | Description |
|
| |---|---:|---:|---|
|
| | `wells_master.csv` | 500 | 9 | Well spine: basin, formation, completion date, lateral, decline model + Arps params (qi/di/b) |
|
| | `production_timeseries.csv` | 180,000 | 5 | Monthly oil/gas/water production over 30 years (360 timesteps per well) |
|
| | `decline_parameters.csv` | 500 | 5 | Standalone Arps parameter table: decline_model + qi_bpd + di + b_factor |
|
| | `eur_forecasts.csv` | 500 | 3 | 30-year cumulative oil + 4-class SPE PRMS reserve category (PDP/PUD/Probable/Possible) |
|
| | `pressure_depletion.csv` | 180,000 | 4 | Monthly reservoir pressure + flowing BHP (BHP = 0.75 × reservoir per drawdown convention) |
|
| | `artificial_lift_events.csv` | 213 | 4 | ~42% of wells: single lift install event (ESP/Gas Lift/Rod Pump/Plunger Lift) |
|
| | `production_interruptions.csv` | 96 | 3 | ~18% of wells: single 3-class interruption (Shut-In/Compressor Failure/Workover) |
|
|
|
| Total: **361,809 rows** across 7 CSVs, ~14.7 MB on disk.
|
|
|
| ---
|
|
|
| ## Calibration: industry-anchored, honestly reported
|
|
|
| Validation uses a **10-metric scorecard** with targets sourced exclusively to
|
| **named industry standards**: **Arps (1945) JPT** "Analysis of Decline Curves"
|
| (canonical decline equation), **SPE PEH Vol V** (production engineering),
|
| **SPE 167242** (Lee & Sidle b-factor calibration for unconventional wells),
|
| **SPE 152596** (Unconventional Reservoir DCA), **SEC 2008 Modernization of
|
| Oil and Gas Reporting** (reserve definitions), **SPE PRMS 2018** (Petroleum
|
| Resources Management System reserve classification), EIA AEO 2023 US
|
| unconventional well economics, Rystad ShaleWellCube well-level economics.
|
|
|
| **Sample run** (seed `42`, n_wells=500, months=360):
|
|
|
| | # | Metric | Observed | Target | Tolerance | Status | Source |
|
| |---|---|---:|---:|---:|---|---|
|
| | 1 | avg qi bpd | 954.0747 | 950.0 | ±200.0 | ✓ PASS | EIA AEO + Rystad ShaleWellCube — mean initial oil rate (qi) for mixed US shale + conventional portfolio (Permian / Eagle Ford / Bakken / Marcellus IP rates 500-2000 BOPD; conventional ~300-800 BOPD) |
|
| | 2 | avg di annual | 0.7822 | 0.78 | ±0.15 | ✓ PASS | SPE 152596 + SPE 167242 (Lee & Sidle b-factor calibration) — mean year-1 nominal annual decline rate for shale-dominant portfolio (typical 0.60-0.85 yr1 for unconventional, 0.10-0.30 for conventional) |
|
| | 3 | avg hyperbolic b factor | 1.1491 | 1.15 | ±0.3 | ✓ PASS | Arps (1945) JPT + SPE 167242 (Lee & Sidle) — mean Arps b-factor for hyperbolic-mode unconventional wells (b > 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<b<2): q(t) = qi / (1 + b·di·t)^(1/b)
|
|
|
| The sample observes exactly 33% in each mode (uniform ML-balanced
|
| sampling). Hyperbolic b-factor centers on 1.15 per **SPE 167242 (Lee &
|
| Sidle)** unconventional calibration (b > 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+
|
| |
