20 Oil & Gas Onboarding Questionnaire – Kav AI Document Portfolio

20.1 Organization Information

Company / Operator Name: _____________________________________________
Division / Business Unit: _____________________________________________
Primary Contact Name & Title: ________________________________________
Primary Contact Email & Phone: _______________________________________
Secondary Contact Name & Email: _____________________________________
Headquarters Address: _________________________________________________

20.2 Asset & Facility Overview

20.2.1 Industry Segment

Select all segments that apply to the target assets:

Upstream: Exploration, drilling, production, wellheads, gathering lines.
Midstream: Transmission pipelines, compressor stations, pump stations, storage terminals, metering.
Downstream: Refining, petrochemical processing, distribution, storage.
Offshore: Platforms, FPSOs, subsea infrastructure.

20.2.2 Target Facility Details

Facility / Asset Name & Site ID: _______________________________________
Physical Location (GPS or City, State/Province, Country): _________________
Geographical Span (e.g., pipeline length in miles/km, total acreage): ________
Year Built & Age of Major Infrastructure: _______________________________
Daily Throughput Capacity (e.g., bpd, mmcfd, tons/day): __________________
Operating Hours & Staffing (e.g., 24/7 continuous, unmanned remote): _______
Number of Remote Sites / Wellheads / Valve Stations: ____________________

20.2.3 Asset Inventory

Check all asset types that will be monitored by the Kav AI platform:

Wellheads & Production Pads: Christmas trees, artificial lift pumps (sucker rod, ESP).
Transmission Pipelines: Above-ground corridors, buried pipeline crossings, river crossings.
Storage Infrastructure: Crude oil tanks, LPG/LNG storage, pressure vessels, slop tanks.
Process Equipment: Separators (2-phase/3-phase), heat exchangers, heater treaters, dehydrators.
Compressors & Pumps: Reciprocating compressors, centrifugal compressors, main line injection pumps.
Safety & Environmental: Flare stacks, thermal oxidizers, vapor recovery units (VRU).
Logistics & Terminals: Loading racks (truck/rail/marine), metering skids, pig launchers/receivers.
Auxiliary & Control: Control rooms, MCC buildings, electrical substations, emergency generators.

20.3 Distributed Sites and Remote Assets

Many oil and gas operations rely on a vast network of remote and unmanned assets. Please describe your remote site landscape below:

Total Number of Unmanned Sites: ______________________________________
Geographic Coverage Area (sq. miles/km): ______________________________
Typical Communication Backhaul (Cellular LTE, Satellite, Licensed Radio, Fiber): _________________
Average Distance / Travel Time Between Sites: ___________________________
Current Remote Monitoring Capabilities: _________________________________

20.3.1 Remote Site Challenges

Check all operational challenges you experience with distributed assets:

Limited or intermittent communication bandwidth.
Delayed awareness of critical equipment failures or shutdowns.
Security concerns, including vandalism, copper theft, or unauthorized access.
Difficult physical access due to terrain, weather, or seasonal road restrictions.
Aging infrastructure with undocumented configuration changes.
Environmental exposure (e.g., extreme heat, deep freeze, coastal corrosion).
Off-grid power management issues (e.g., battery degradation, solar panel fouling).
High cost of sending technicians for simple verification or “false alarms”.

20.4 Extreme Weather & Pipeline Event Operations

Kav AI overlays real-time weather and environmental data with asset telemetry to provide advanced situational awareness.

What weather triggers are most critical for your assets? (e.g., freeze-offs, hurricanes, extreme wind, lightning) __________________________________________________________________________
How are pipeline valve closures or system shut-ins decided during weather events? __________________________________________________________________________
What data sources do operators currently correlate under high-pressure events? __________________________________________________________________________

20.4.1 Weather & Operational Pain Points

Check all challenges that impact your operations during weather or pressure upsets:

Freeze-Offs: Sudden wellhead or instrument line freeze-ups leading to unplanned shut-ins.
Storm Preparedness: Deploying field crews to remote areas “just in case,” exposing them to hazards.
Siloed Systems: Operators must cross-reference weather forecasts, SCADA pressures, and GIS locations manually.
Pipeline Slack Line / Surge: Difficulty distinguishing real pressure/flow anomalies from transient noise during start/stops.
Methane & Flare Management: High wind causing flare flame-outs, leading to uncombusted methane releases.
Post-Event Audit: Hours spent reconstructing timeline events and identifying which assets experienced the highest stress.

20.5 Control Systems & OT Infrastructure

20.5.1 SCADA, DCS, & Historian

SCADA/DCS Vendor & Product Name: ____________________________________
SCADA/DCS Software Version & Year: _________________________________
Historian in Use (e.g., OSISoft PI, AspenTech IP21, Honeywell PHD): _________
Data Polling and Archival Frequency (e.g., 1s, 5s, 10s): _________________

20.5.2 PLC & Flow Computer Inventory

List primary controller types (e.g., Allen-Bradley ControlLogix, Siemens S7-1500, Emerson ROC, ABB Totalflow):

Controller Vendor & Model: ___________________________________________
Approximate Quantity: _______________________________________________
Age Range: ___________________________________________________________

20.6 Alarm Management & Operator Workflows

20.6.1 Alarm Volume & Fatigue

Approximate Number of Configured Alarms: ______________________________
Average Alarms per Day (Normal Operations): ___________________________
Average Alarms per Day (Upset Conditions / Storms): ___________________
Estimated Percentage of “Nuisance” Alarms: ________%

20.6.2 Operator Workflow Challenges

Check all issues that impact your control room operators:

Operators routinely acknowledge/silence alarms without investigating due to volume.
Critical safety or leak warnings can be missed during an “alarm flood” event.
No automated correlation between process alarms (e.g., pressure drop) and physical events (e.g., thermal anomaly).
Shift changes are vulnerable to loss of situational awareness (“tribal knowledge” gap).
No clear, automated escalation path when a high-priority alarm remains unacknowledged.

20.7 Security, Network Architecture & Compliance

Kav AI utilizes a read-only, security-first integration architecture to guarantee that no control actions can be back-propagated into your OT environment.

20.7.1 Security Posture

Is there physical and logical OT/IT network segmentation? [ ] Yes [ ] No
Is there a Demilitarized Zone (DMZ) with a jump host? [ ] Yes [ ] No
Firewall Vendor(s) in Use: ____________________________________________
Do you allow outbound-only connections to secure cloud platforms? [ ] Yes [ ] No

20.7.2 Cybersecurity & Industry Standards

Check all standards that apply to your asset cybersecurity review:

NIST CSF (Cybersecurity Framework)
NIST SP 800-82 (Industrial Control Systems Security)
IEC 62443 (Parts 2-4 / 3-3)
API RP 1164 (Pipeline Cybersecurity)
TSA Pipeline Security Guidelines
CFATS (Chemical Facility Anti-Terrorism Standards)
SOC 2 Type II Vendor Compliance
Internal Cybersecurity Policy

20.8 Data Governance & Sovereignty

Are there geographical residency requirements for your operational data? __________________________________________________________________________
What is your mandatory data retention period for historical telemetry and video? __________________________________________________________________________
Do process recipes, field layouts, or telemetry labels contain proprietary trade secrets? __________________________________________________________________________

20.9 Operational Metrics (ROI Baseline)

To measure the ROI of the Kav AI platform, we establish a baseline using your historical metrics. Estimates are acceptable where precise figures are unavailable.

Unplanned Facility / Line Downtime Incidents per Year: __________________
Average Duration of a Downtime Event (Hours): _________________________
Estimated Cost per Hour of Production Downtime ($/hr): _________________
Number of Emergency Field Callouts (Roll-offs) per Month: ________________
Percentage of Maintenance Budget Spent on Reactive / Emergency Repairs: _____%
Annual Cost of Regulatory Environmental Fines / Methane Audits: _________

20.10 Inspection Practices & Sensor Capabilities

20.10.1 Inspection Methods

Check all methods currently used to assess asset integrity:

Manual visual walk-arounds / operator rounds.
Drone-based aerial inspections (automated or manual).
Thermal imaging / Infrared (IR) camera sweeps (for insulation/tank level).
Optical Gas Imaging (OGI) cameras (for fugitive methane leaks).
Handheld gas sniffers (FID, PID, laser).
Ultrasonic thickness testing (UT) for pipeline/vessel wall corrosion.
Cathodic protection survey measurements.
In-line inspection (ILI / Smart Pigging).
Vibration monitoring (continuous or periodic) on pumps and compressors.

20.10.2 Asset Monitoring Equipment Inventory

Please list equipment owned or contracted:

UAV / Drone Platforms: ________________________________________________
Thermal / IR Cameras: _________________________________________________
OGI Cameras: __________________________________________________________
Continuous Gas Detection Sensors: _______________________________________

20.11 Goals & Strategic Objectives

Rank your top priorities for implementing Kav AI (1 = highest priority, 12 = lowest):

Objective	Priority (1-12)
Reduce fugitive methane emissions (EPA/LDAR compliance)
Prevent pipeline rupture and product leaks
Minimize unplanned pump/compressor downtime
Improve flare stack combustion efficiency monitoring
Reduce control room operator alarm fatigue
Elevate safety during extreme weather events (freeze-offs)
Track tank roof and seal integrity using thermal imaging
Extend the operating life of legacy assets
Centralize visibility across multi-basin, distributed assets
Enable predictive maintenance on rotating machinery
Transition from manual visual rounds to automated AI exceptions
Build an interactive 3D digital twin of processing plants

20.12 Regulatory Compliance

Check all applicable environmental, health, and safety regulatory requirements:

EPA OOOOa / OOOOb / OOOOc (Quad O): Methane and VOC emission monitoring.
EPA Subpart W: Greenhouse gas reporting for petroleum and natural gas systems.
OSHA PSM (1910.119): Process Safety Management of highly hazardous chemicals.
PHMSA (Title 49 CFR Part 190-199): Pipeline and hazardous materials safety standards.
State Environmental Protection Rules: (e.g., Texas TCEQ, Colorado CDPHE, California CARB).
Local Air Quality Permits: Specific stack, vent, or fugitive limits.

20.13 IT & Enterprise Integration

Enterprise Asset Management (EAM) / CMMS (e.g., SAP PM, IBM Maximo): __________________________________________________________________________
Geographic Information System (GIS) (e.g., Esri ArcGIS): __________________________________________________________________________
Laboratory Information Management System (LIMS) (for gas/crude assays): __________________________________________________________________________
Do you require Single Sign-On (SSO) integration (SAML 2.0 / OIDC)? __________________________________________________________________________

20.14 Proposed Pilot Scope

Help us define a highly focused pilot deployment to demonstrate immediate value:

Target Pilot Location (e.g., West Texas Gathering Block A, Refining Unit 4): __________________________________________________________________________
Primary Asset(s) to Monitor (e.g., 3 compressors, 5 storage tanks, 12 wellheads): __________________________________________________________________________
Specific Problem to Solve in the Pilot: (e.g., “Detect fugitive methane leaks on unstaffed pads within 30 minutes of occurrence”) __________________________________________________________________________
Target Start Date & Proposed Pilot Duration (e.g., 90 days): __________________________________________________________________________
Key Stakeholders for Pilot Evaluation: __________________________________________________________________________

20.15 Submission & Sign-off

By submitting this onboarding questionnaire, I confirm that the information provided represents our operational goals and infrastructure to the best of my knowledge.

Printed Name & Title: __________________________________________________
Authorized Signature: __________________________________________________
Date: __________________________

Please return the completed document to your Kav AI Solutions Architect at info@kavai.com.