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Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
基于 SOC 职业分类
正在显示 SKILL.md
| name | drillbotics-1-trajectory-planner |
| description | Sub-skill of drillbotics: 1. Trajectory Planner (+9). |
| version | 1.1.0 |
| category | engineering/drilling |
| type | reference |
| scripts_exempt | true |
3D directional well-path computation from surface to multiple downhole targets. Algorithms used by winning teams: cubic Bezier curves, minimum-curvature method. Constraints: dogleg severity (DLS) limits, survey station uncertainty propagation.
# Key inputs
targets: list[Point3D] # revealed on competition day
dls_max: float # deg/100ft — formation / BHA constraint
survey_uncertainty: float # typical ±0.5° inclination, ±1° azimuth
# Key outputs
planned_path: list[SurveyStation] # MD, inclination, azimuth at each station
Closed-loop steering of the simulated bit along the planned path. Must correct for BHA model uncertainty at each survey station. Reinforcement learning has been used by winning teams (UiS 2021-2022) for generalization to unseen target geometries.
Critical constraint: BHA type (Rotary Steerable System vs Adjustable Kick-Off motor) is specified on competition day. Both must be pre-implemented and switchable.
Open-source references:
MADSim-Wilson-Simple-BHA-Model (Open Source Drilling Community)Aarsnes-and-Shor-Torsional-Model (torsional vibration)Real-time, formation-dependent optimisation of WOB and RPM to maximise ROP without causing dysfunctions. Typically a nonlinear constrained optimisation problem.
Bourgoyne-Young model (eight-parameter, industry standard):
ROP = f(WOB, RPM, d_bit, ECD, formation_coefficients, bit_wear, overbalance, compaction)
Warren model (simplified, two-parameter):
ROP = K * (WOB / d_bit)^a * RPM^b
Status in ACE Engineering: NOT YET IMPLEMENTED. Gap identified in WRK-375.
Candidate location: digitalmodel/well/drilling/rop_model.py
Drillstring friction and axial/torque loads along the wellbore trajectory. Used for WOB management and stuck-pipe detection.
Open-source reference: Dixit-Drillstring-Model-2023 (Open Source Drilling Community)
Status in ACE Engineering: NOT YET IMPLEMENTED.
Managed pressure drilling: keep bottomhole pressure inside the mud-weight window.
| Parameter | Formula | Purpose |
|---|---|---|
| ECD | MW + (annular_ΔP / (0.052 × TVD)) | Formation overbalance control |
| Annular velocity | Q / (d_hole² − d_pipe²) × 0.408 | Cuttings transport |
| Pump pressure | Fanning friction + acceleration components | Surface pump management |
Status in ACE Engineering: PARTIAL — CT hydraulics exists at
digitalmodel/src/digitalmodel/marine_ops/ct_hydraulics/ct_hydraulics.py
(ECD, pressure drop, Reynolds/friction, pump pressure). Needs generalisation to
arbitrary pipe/annulus diameters and multi-phase flow. Gap: ~250 lines to extend.
Kick detection and automated shut-in. Scored as a separate category.
Detection signals:
False-alarm rate is explicitly scored — conservative triggers that shut in unnecessarily count against the team.
Real-time identification of formation being drilled from surface drilling signals (WOB, ROP, torque, vibration). Enables adaptive control parameter switching.
UMaT (2024 winner) used ensemble ML methods + a Python-based digital-twin architecture.
Human-in-the-loop interface required since 2021. Criteria:
All modules must expose data via Drilling Well Information Schema (D-WIS) semantic network protocols. This is a mandatory gate for Phase II entry in the 2025-2026 cycle.
Reference: https://d-wis.org