The oil and gas industry generates some of the most complex technical documentation in the world: well logs, seismic interpretations, drilling reports, regulatory filings, safety incident narratives, and reservoir simulation outputs. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can process this specialized, data-intensive environment to accelerate analysis, ensure compliance, and optimize operations. TokenEase's unified API provides energy companies with cost-effective access to these powerful models for upstream, midstream, and downstream applications.
Interpret well log data, core descriptions, and geological context to generate reservoir characterization narratives and production recommendations.
import requests
def characterize_reservoir(well_logs, core_data, geological_context):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a senior reservoir engineer. Interpret well logs and core data to characterize reservoirs, estimate porosity/permeability trends, and recommend completion strategies."},
{"role": "user", "content": f"Geological context: {geological_context}\nWell logs:\n{well_logs}\nCore data:\n{core_data}\n\nCharacterize reservoir and recommend completions."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
logs = """
Depth 2150-2180m: Gamma 45-55 API, Resistivity 15-25 ohm-m, Neutron 28-32%, Density 2.35-2.45 g/cc
Depth 2180-2200m: Gamma 35-40 API, Resistivity 45-80 ohm-m, Neutron 22-26%, Density 2.30-2.38 g/cc
"""
core = "2155m: Porosity 18%, Permeability 85 mD, Oil saturation 65%. 2190m: Porosity 22%, Permeability 210 mD, Oil saturation 78%."
context = "Miocene sandstone reservoir, offshore field, water depth 120m, normal pressure regime"
char = characterize_reservoir(logs, core, context)
Analyze drilling parameters and mud logs to identify problems early and recommend adjustments to improve rate of penetration and reduce non-productive time.
def analyze_drilling_performance(drill_parameters, mud_properties, problems_encountered):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze drilling data to identify problems, predict stuck pipe risks, and recommend parameter changes to optimize ROP and minimize NPT."},
{"role": "user", "content": f"Parameters: {drill_parameters}\nMud: {mud_properties}\nProblems: {problems_encountered}\n\nAnalyze and recommend actions."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
params = "WOB: 25-30 klbs, RPM: 120, Flow rate: 850 gpm, ROP: 12-15 ft/hr declining to 8 ft/hr"
mud = "MW: 14.2 ppg, PV: 45 cp, YP: 12 lb/100ft2, Gels: 18/28"
problems = "Increasing torque past 3 days. Shale shaker showing fine cuttings increase. Gas shows at 18,500 ft."
analysis = analyze_drilling_performance(params, mud, problems)
Generate comprehensive incident investigation reports from field observations, witness statements, and equipment data.
def generate_hse_report(incident_description, witness_statements, equipment_data, photos_descriptions):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Generate professional HSE incident investigation reports following industry best practices (IOGP, API). Include root cause analysis and corrective actions."},
{"role": "user", "content": f"Incident: {incident_description}\nWitnesses: {witness_statements}\nEquipment: {equipment_data}\nPhotos: {photos_descriptions}\n\nGenerate investigation report."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
incident = "Blowout preventer (BOP) leak during pressure test at 10,000 psi. No injuries. Operations suspended."
witnesses = "Driller: Noticed pressure drop over 30 seconds. Toolpusher: Confirmed annular preventer seal failure."
equipment = "BOP: 13-5/8\" 10M annular preventer, last tested 45 days ago, certified until 2026-12"
photos = "Photo 1: Hydraulic fluid leak at annular piston housing. Photo 2: Pressure chart showing gradual decline."
report = generate_hse_report(incident, witnesses, equipment, photos)
Automate the generation of regulatory submissions, environmental permits, and compliance reports across multiple jurisdictions.
def generate_permit_application(project_details, environmental_baseline, regulatory_requirements):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "Generate oil and gas permit applications and regulatory compliance documents. Include all required sections and technical justifications."},
{"role": "user", "content": f"Regulations: {regulatory_requirements}\nProject: {project_details}\nEnvironment: {environmental_baseline}\n\nGenerate permit application."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
project = "Offshore exploratory well, Block A-12, water depth 85m, planned TD 4,200m MD"
env = "Coral reef sensitivity zone 5km east. Marine mammal migration season March-May. Fisheries active in area."
regs = "EPA offshore drilling permit, NPDES discharge permit, BOEM lease compliance, state coastal commission approval"
permit = generate_permit_application(project, env, regs)
Analyze maintenance logs, vibration data narratives, and inspection reports to predict equipment failures before they occur.
def predict_equipment_failure(equipment_type, maintenance_history, recent_anomalies, operating_conditions):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Analyze equipment data to predict failures, estimate remaining useful life, and recommend maintenance actions for critical oilfield equipment."},
{"role": "user", "content": f"Equipment: {equipment_type}\nConditions: {operating_conditions}\nHistory: {maintenance_history}\nAnomalies: {recent_anomalies}\n\nPredict failure risk and recommend actions."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
equip = "Gas compressor, 5,000 HP, centrifugal, 3-stage"
conditions = "Operating 8,500 hrs since last overhaul. Sour gas service (2% H2S). Inlet pressure declining 5% over 6 months."
history = "Last overhaul: 2024-03. Impeller replacement in 2023. Seal replacement every 18 months."
anomalies = "Vibration trending up on DE bearing. Seal leakage rate doubled past 2 weeks. Intercooler delta-T increased 8F."
prediction = predict_equipment_failure(equip, history, anomalies, conditions)
Synthesize news, regulatory changes, and market data into actionable intelligence for trading and strategic planning.
def analyze_market_intelligence(news_feeds, regulatory_changes, price_data, competitor_activity):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze oil and gas market intelligence. Identify trends, risks, and opportunities. Provide concise executive summaries with actionable recommendations."},
{"role": "user", "content": f"Prices: {price_data}\nNews: {news_feeds}\nRegulations: {regulatory_changes}\nCompetitors: {competitor_activity}\n\nGenerate market intelligence brief."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
news = "OPEC+ considering production increase of 500k bpd. Libya exports disrupted due to port closure."
regs = "EU carbon border tax expanding to LNG imports from 2027. US EPA methane rules finalized."
prices = "Brent: $82.50/bbl. WTI: $78.20/bbl. Henry Hub: $2.85/MMBtu."
competitors = "Shell divesting Nigerian onshore assets. Exxon increasing Permian rig count by 8."
intel = analyze_market_intelligence(news, regs, prices, competitors)
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