README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- upstream
- enhanced-oil-recovery
- waterflood
- water-injection
- reservoir-engineering
- sweep-efficiency
- breakthrough-prediction
- voidage-replacement
- xpertsystems
pretty_name: "OIL-017 — Synthetic Water Injection Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-017 — Synthetic Water Injection Dataset (Sample)

**SKU:** `OIL017-SAMPLE` · **Vertical:** Oil & Gas / Upstream Enhanced Oil Recovery
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil017.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise water
injection / waterflood dataset for sweep efficiency ML, breakthrough
prediction, conformance optimization, and Voidage Replacement Ratio (VRR)
analytics. The sample covers **80 injectors** with
**359 injector-producer connectivity links** across **8
global basins** and **5 flood pattern types**, simulated over
**3,650 days (10 years)**, with **136,579 rows** linked across
**11 tables**.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `injection_wells.csv` | 80 | 20 | Injector spine: basin, pattern, perm/poro/thickness, mobility ratio, injection rate, water source |
| `connectivity_matrix.csv` | 359 | 13 | Injector-producer pairs: distance, connectivity score, communication delay, fracture-assisted flag |
| `producer_response.csv` | 43,798 | 12 | Per-link-per-timestep oil/water rate + water cut + logistic post-breakthrough water rise |
| `reservoir_pressure.csv` | 43,798 | 9 | Per-link pressure timeseries + VRR + pressure support efficiency |
| `sweep_efficiency.csv` | 43,798 | 10 | Areal + vertical + displacement sweep over time, mobility-ratio-modulated per Buckley-Leverett |
| `breakthrough_events.csv` | 359 | 9 | One event per link: severity + post-breakthrough water cut + channeling-suspected flag |
| `reservoir_labels.csv` | 359 | 9 | ML labels: 4-class sweep quality grade (A/B/C/D) + 3-class flood efficiency + 3-class breakthrough risk + recovery factor |
| `injection_profiles.csv` | 376 | 8 | Multi-layer Dirichlet allocation per injector (sums to 100%) + per-layer injectivity index |
| `water_quality.csv` | 3,280 | 10 | Quarterly water samples: salinity / sulfate / hardness / bacteria / scaling risk per NACE SP0192 |
| `conformance_events.csv` | 13 | 9 | Sparse channeling events: 6-class treatment (gel polymer / profile mod / selective shutoff / polymer flood / low salinity) + effectiveness |
| `production_forecasts.csv` | 359 | 9 | Per-link 5-year EUR + recovery factor + terminal water cut + economic limit flag |

Total: **136,579 rows** across 11 CSVs, ~14.1 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **Buckley-Leverett (1942)** fractional flow
theory, **Welge (1952)** displacement efficiency, **Dykstra-Parsons (1950)**
heterogeneity index, **Craig (1971) SPE Monograph 3** "Reservoir Engineering
Aspects of Waterflooding" (the canonical waterflood reference), SPE PEH Vol V
(production engineering), **SPE 14529** (Voidage Replacement Ratio guidance),
Willhite (1986) "Waterflooding" SPE textbook, SPE 13855 (sweep efficiency
models), **Caudle (1968)** flood pattern geometries, NACE SP0192 (oilfield
water quality for injection), Rystad Energy + IHS Markit global waterflood
operations tracker.

**Sample run** (seed `42`, n_injectors=80, simulation_days=3,650):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg voidage replacement ratio | 0.9799 | 0.98 | ±0.1 | ✓ PASS | SPE 14529 (Voidage Replacement Ratio guidance) + SPE PEH Vol V — mean VRR for mature waterflood portfolio (target 0.95-1.05 for pressure maintenance; <0.90 indicates under-injection, >1.10 over-injection) |
| 2 | avg mobility ratio | 1.1499 | 1.15 | ±0.4 | ✓ PASS | Buckley-Leverett (1942) fractional flow theory + Craig (1971) SPE Monograph 3 — mean end-point mobility ratio for mixed sandstone/carbonate/heavy-oil portfolio (M < 1 favorable, M > 1 unfavorable; typical 0.5-2.5) |
| 3 | avg reservoir pressure psi | 3779.8925 | 3850.0 | ±600.0 | ✓ PASS | SPE PEH Vol V + IHS Markit global waterflood tracker — mean reservoir pressure for mature waterflood portfolio (typical 2500-5500 psi at injection-supported equilibrium) |
| 4 | avg injection rate bwpd | 14235.8650 | 13000.0 | ±4000.0 | ✓ PASS | Rystad Energy + IHS Markit + SPE PEH Vol V — mean water injection rate for mixed global waterflood operations (typical 5,000-25,000 BWPD per injector for moderately-thick reservoirs) |
| 5 | avg water salinity ppm | 84378.8965 | 85000.0 | ±25000.0 | ✓ PASS | NACE SP0192 (oilfield water quality for injection) + SPE 14529 — mean water salinity for mixed produced-water + seawater portfolio (formation water 50,000-200,000 ppm TDS; seawater ~35,000 ppm) |
| 6 | avg composite sweep efficiency | 0.8199 | 0.75 | ±0.15 | ✓ PASS | Craig (1971) SPE Monograph 3 + Welge (1952) — mean composite sweep efficiency (geometric mean of areal × vertical × displacement) for mature waterflood portfolio (0.45-0.75 typical at flood maturity) |
| 7 | connectivity delay pearson correlation | -0.8096 | -0.7 | ±0.2 | ✓ PASS | Craig (1971) + SPE 13855 sweep efficiency models — expected strong inverse correlation between injector-producer connectivity score and communication delay (physics: high connectivity = short delay = fast breakthrough propagation) |
| 8 | mobility displacement pearson correlation | -0.9530 | -0.85 | ±0.15 | ✓ PASS | Buckley-Leverett (1942) + Craig (1971) — expected strong inverse correlation between mobility ratio (M) and displacement efficiency (E_d ∝ 1/M^0.35 per fractional flow theory; M > 1 = unfavorable mobility = reduced displacement) |
| 9 | injection profile completeness | 1.0000 | 1.0 | ±0.02 | ✓ PASS | SPE production allocation guidelines + IOGP injection profile standards — per-injector multi-layer injection allocations must sum to 100% (validates Dirichlet sampling produces complete profiles) |
| 10 | flood pattern diversity entropy | 0.9353 | 0.91 | ±0.06 | ✓ PASS | Caudle (1968) flood pattern geometries + Craig (1971) SPE Monograph 3 — 5-class flood-pattern diversity benchmark (5-spot, 9-spot, line drive, peripheral, inverted 5-spot; 5-spot dominant per industry default weights [0.34, 0.18, 0.20, 0.18, 0.10]), normalized Shannon entropy |

**Overall: 100.0/100 — Grade A+**
(10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

---

## Schema highlights

**`injection_wells.csv`** — injector spine with **basin-conditioned
heterogeneity priors** per Craig (1971):

> heterogeneity_index = basin_base + N(0, 0.08)  clip(0.05, 0.85)
> mobility_ratio      = lognormal(log(1.15), 0.30) clip(0.35, 4.5)
> injection_pressure  = reservoir_P + N(850, 270) clamped to (P_res + 80, P_frac − 120)

Injection pressure is **always between reservoir pressure and fracture
pressure** — preventing physically-impossible "fractured injection above
parting pressure" scenarios.

**`connectivity_matrix.csv`** — injector-producer pairs with **Poisson-
sampled producers per injector** (mean 4, pattern-conditioned). Communication
delay follows the canonical Craig (1971) relation:

> delay = base_delay × (distance / 1800 ft)^0.25 / max(connectivity, 0.34)

The connectivity↔delay Pearson correlation is r ≈ −0.81 in the sample —
strong inverse coupling confirms Craig (1971) physics.

**`producer_response.csv`** — per-link-per-timestep production with **logistic
post-breakthrough water rise**:

> Pre-breakthrough:  water_cut = 18 + 20·(t/T_total) + N(0, 1.5)
> Post-breakthrough: water_rise = 1 / (1 + exp(−3.2·(post/720 − 0.5)))
>                    water_cut = 24 + (max_WC − 24) × water_rise

This is **Buckley-Leverett-style sigmoid breakthrough behavior** — slow
ramp-up, steep rise around mid-breakthrough, asymptotic approach to terminal
water cut. The `breakthrough_flag` column is the canonical binary
classification target.

**`reservoir_pressure.csv`** — **VRR-driven pressure depletion + injection
support** per SPE 14529:

> depletion     = 0.00012 × day × (1.05 − VRR) × P_initial
> support_gain  = 180 × (1 − exp(−day/900)) × max(0, VRR − 0.86)
> pressure(t)   = P_initial − depletion + support_gain + noise

VRR centered on 0.98 (target balanced injection); VRR > 0.86 produces net
support gain. The sample's VRR mean is 0.980 — bullseye for SPE 14529
balanced-waterflood operations.

**`sweep_efficiency.csv`** — Buckley-Leverett / Craig (1971) sweep model with
**three components**:

> areal       = 0.60 + 0.28·(1 − exp(−day/1200)) − 0.10·heterogeneity + noise
> vertical    = 0.56 + 0.26·(1 − exp(−day/1500)) − 0.08·heterogeneity + noise
> displacement = 0.66 + 0.20 / mobility_ratio^0.35 − 0.04·heterogeneity

The mobility-ratio↔displacement correlation is r ≈ −0.95 — **near-perfect
inverse coupling per Buckley-Leverett (1942) fractional flow theory**
(M > 1 = unfavorable mobility = reduced displacement).

**`injection_profiles.csv`** — multi-layer Dirichlet allocation per injector
with **heterogeneity-driven concentration**:

> alpha = 1.5 + 2.0·(1 − heterogeneity)
> fractions ~ Dirichlet(alpha × ones(n_layers))

Per-injector fractions sum to exactly 100%. High-heterogeneity reservoirs
get more spread-out (low-α) distributions; low-heterogeneity reservoirs get
more even distributions.

**`reservoir_labels.csv`** — 4-class sweep grade per Welge (1952)
displacement efficiency benchmarks:

| Grade | Threshold (composite sweep) |
|---|---|
| `A` | ≥ 0.72 |
| `B` | 0.62 ≤ sweep < 0.72 |
| `C` | 0.50 ≤ sweep < 0.62 |
| `D` | < 0.50 |

---

## Suggested use cases

1. **Breakthrough timing regression** — predict
   `breakthrough_time_days` from connectivity score + distance +
   heterogeneity features. **Very strong physics signal**: r ≈ −0.81
   connectivity↔delay coupling.
2. **Composite sweep efficiency regression** — predict end-of-flood
   sweep from mobility ratio + heterogeneity features. **Near-perfect
   physics signal**: r ≈ −0.95 mobility↔displacement coupling.
3. **4-class sweep quality grade classification** — ordinal classifier
   (A/B/C/D) on sweep grade — useful as label-only reference; see
   Honest Disclosure §1 for sample-scale class-imbalance caveat.
4. **3-class breakthrough risk classification** — multi-class
   classifier (low/medium/high) from upstream operational features.
   Less imbalanced than sweep grade.
5. **Channeling/thief zone detection** — binary classifier on
   `channeling_suspected_flag` from connectivity + heterogeneity
   features.
6. **VRR optimization** — regression on
   `voidage_replacement_ratio` to identify under/over-injection
   scenarios per SPE 14529.
7. **Scaling risk prediction** — regression on
   `scaling_risk_score` from water-quality features (salinity /
   sulfate / hardness) per NACE SP0192.
8. **Conformance treatment prediction** — multi-class classifier on
   `treatment_type` from channeling/heterogeneity features (note:
   sparse table, see Honest Disclosure §3).
9. **EUR forecasting** — regression on
   `estimated_ultimate_recovery_bbl` per injector-producer link
   from operational history features.
10. **Multi-table relational ML** — entity-resolution and graph
    neural-network learning across the 11 joinable tables via
    `injector_id` + `producer_id`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil017-sample", data_files="producer_response.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
inj    = pd.read_csv("hf://datasets/xpertsystems/oil017-sample/injection_wells.csv")
conn   = pd.read_csv("hf://datasets/xpertsystems/oil017-sample/connectivity_matrix.csv")
prod   = pd.read_csv("hf://datasets/xpertsystems/oil017-sample/producer_response.csv")
sweep  = pd.read_csv("hf://datasets/xpertsystems/oil017-sample/sweep_efficiency.csv")
labels = pd.read_csv("hf://datasets/xpertsystems/oil017-sample/reservoir_labels.csv")

# Join sweep timeseries to injector mobility ratio for Buckley-Leverett ML:
sweep_full = sweep.merge(inj[["injector_id", "mobility_ratio", "heterogeneity_index"]], on="injector_id")
# Now you have displacement_efficiency ↔ mobility_ratio for sweep ML
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
`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 for waterflood / EOR ML research, not for live
reservoir-management decisions. Several important notes:

1. **Sweep quality grade is heavily skewed toward A at sample scale.** The
   10-year simulation horizon drives most links to mature/high sweep state
   (composite sweep ~0.82 mean, well above the 0.72 grade-A threshold). All
   ~360 sample links classify as grade A and flood_efficiency_class=high in
   the seed-42 run. **The 4-class sweep classification task is degenerate at
   sample scale.** Use `recovery_factor_pct` as a continuous regression
   target instead, or use the `breakthrough_risk_class` (which has all three
   classes populated: ~59% medium / ~26% high / ~14% low). The full product
   (45K injectors, longer / mixed simulation durations) gives proper
   class diversity.

2. **`optimization_priority` is all "standard"** in the sample because the
   "urgent" condition requires grade C/D AND severity > 0.60, and no rows
   trip grade C/D at this simulation maturity. Treat this column as
   reference-only at sample scale.

3. **Conformance events are extremely sparse** (~3.6% of links at sample
   scale, ~13 events for 359 links). Conformance treatment ML on this
   sample will not have enough positive examples for robust classifier
   training. For conformance ML at sample scale, use the
   `channeling_suspected_flag` in `breakthrough_events.csv` (synthesized
   from severity, more populated) rather than the literal conformance
   events table. The full product generates orders of magnitude more
   conformance events.

4. **Breakthrough rate is ~75% by end of simulation** because the 3650-day
   (10-year) horizon exceeds 4× the median breakthrough time (~900 days).
   This is **physically correct for mature waterfloods** but means the
   `breakthrough_flag` column saturates over time. For early-breakthrough
   ML, filter to `day < median_breakthrough_time` (~900 days) to study
   pre-breakthrough → breakthrough transitions.

5. **Heterogeneity ↔ sweep coupling is weak in the sample** (r ≈ −0.14
   for areal sweep, r ≈ +0.10 for breakthrough time). The generator
   includes heterogeneity as a small additive modifier rather than a
   strong multiplicative driver — real Dykstra-Parsons V_DP coefficients
   produce much stronger heterogeneity↔sweep coupling. For
   heterogeneity-driven sweep ML, use the full product or post-process
   with V_DP recalibration.

6. **All injection pressure is clamped between reservoir P + 80 and
   fracture P − 120 psi.** This is a deliberate safety constraint
   (preventing physically-impossible above-parting injection) but means
   the `injection_pressure_psi` column has a tight bounded range and
   cannot represent over-pressure / fractured-injection scenarios. For
   above-parting injection ML, manually relax the clamp in the generator.

7. **Mean breakthrough time is 902 days** (cfg target 730) because the
   generator applies a `(distance/1800)^0.25` scaling factor that biases
   delays upward for far-spaced links. The `bre_delay = base_delay ×
   (1.0 + 0.22 × heterogeneity)` formula also extends delays in
   heterogeneous reservoirs. Both are correct physics — but means the
   declared `mean_breakthrough_days=730` is the *base* parameter before
   distance/heterogeneity adjustment, not the actual mean.

8. **Water quality scaling risk averages 0.62** — high because sulfate +
   hardness multipliers compound. Real scaling risk would condition on
   water source (seawater→high sulfate scaling, produced water→high
   barium/strontium). Sample uses uniform-conditioned mineralogy.

---

## Full product

The **full OIL-017 dataset** ships at **45,000 injectors × 10-year
simulation** (prod mode) producing several hundred million producer-
response rows with **Dykstra-Parsons-calibrated heterogeneity coupling**,
**proper 4-class sweep grade diversity** (mixed simulation durations to
populate D/C/B grades), **realistic conformance event rates** (15-25% of
heterogeneous links), and **water-source-conditional mineralogy** —
licensed commercially. Contact XpertSystems.ai for licensing terms.

📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**

---

## Cross-references to other XpertSystems OIL SKUs

This SKU specializes in **waterflood / water injection EOR analytics**.
Related SKUs cover complementary aspects:

| SKU | Focus | Use Case |
|---|---|---|
| **OIL-013** | Production engineering | Daily production with anomaly events, water breakthrough modeling at single-well scale |
| **OIL-014** | Artificial lift performance | ESP / Gas Lift / Rod Pump operations (downstream of waterflood-aided wells) |
| **OIL-016** | Decline curve analysis | Long-horizon Arps DCA + EUR + reserve classification (without waterflood support) |
| **OIL-017** | Water injection / EOR | Injector-producer connectivity + sweep efficiency + breakthrough at field scale (this SKU) |

**OIL-017 vs OIL-013**: OIL-013 simulates **single-well daily production
with operational realism**. OIL-017 simulates **injector-producer pair
dynamics at field scale** with explicit connectivity modeling, sweep
efficiency physics, and VRR-driven pressure maintenance. Use OIL-013 for
well-level ML, OIL-017 for field-scale waterflood optimization ML.

---

## Citation

```bibtex
@dataset{xpertsystems_oil017_sample_2026,
  title  = {OIL-017: Synthetic Water Injection Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil017-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 13:38:39 UTC
- Injectors         : 80
- Simulation days   : 3,650 (10 years)
- Timestep          : 30 days (~monthly)
- Connectivity links: 359
- Basins            : 8 (Permian, North Sea, Middle East, Gulf
                      of Mexico, Brazil Pre-Salt, Canadian Heavy Oil,
                      ADNOC Carbonate, Kuwait Burgan)
- Lithology classes : 4 (sandstone, carbonate, deepwater sand, heavy
                      oil sand)
- Flood patterns    : 5 (5-spot, 9-spot, line drive, peripheral,
                      inverted 5-spot per Caudle 1968)
- Water sources     : 4 (produced water, seawater, aquifer, treated mixed)
- Treatment types   : 6 (none, gel polymer, profile modification,
                      selective shutoff, polymer flood, low salinity)
- Sweep grades      : 4 (A, B, C, D per Welge 1952 thresholds)
- Calibration basis : Buckley-Leverett (1942), Welge (1952),
                      Dykstra-Parsons (1950), Craig (1971) SPE
                      Monograph 3, SPE PEH Vol V, SPE 14529, Willhite
                      (1986), Caudle (1968), NACE SP0192, Rystad,
                      IHS Markit
- Overall validation: 100.0/100 — Grade A+