--- 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: - 100K1.10 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+