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Egypt-UAE Joint Offshore Drilling Concession Analytics Engine

Evaluating expanded joint oil and gas drilling programs between Egypt and the UAE requires processing seismic log summaries and concession yield projections. Build an analytics tool using Python and Pandas to evaluate offshore block profitability and drilling success probability. Reach an evaluation accuracy of 90% against benchmark concession yields.

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What you are building

Build an offshore drilling concession profitability analytics platform using Pandas and Python.

How work is evaluated

Evaluation

Evaluates offshore block financial NPV calculations and risk rank accuracy.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Positive NPV Viability Check

Ensures viable is true if NPV > 0.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

NPV Calculation Accuracy

Accuracy of financial NPV model relative to ground truth. • target: 0.9 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Ingest geological subsurface parameters using Python Pandas

  • Compute Net Present Value (NPV) and Internal Rate of Return (IRR) for offshore blocks

  • Factor in water depth, rig daily rates, and expected reserve recovery (MMBOE)

  • Output comparative concession ranking scorecards

Resources

Dataset notes

Offshore drilling block geological data, rig rates, and reserve estimates for Mediterranean and Red Sea concessions.

Challenge setup

Evaluates offshore block financial NPV calculations and risk rank accuracy.

Challenge input

JSON object with block_id, reserve_mmboe, brent_usd, rig_day_rate_usd

Pandas

Standard Python data manipulation library.

Evaluated output

JSON object with npv_million_usd, viable, rank

Evaluation checks
  • Ensures viable is true if NPV > 0.
  • Accuracy of financial NPV model relative to ground truth. • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: EnergyValBench
  • NPV Calculation Accuracy target: 0.9
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Lyzr · Optional
  • Fixie · Optional
Observation
  • Portkey · Required
Policy Serving
  • Portkey · Required

Evaluation contract

  • Positive NPV Viability Check · Weight 1
  • NPV Calculation Accuracy · Weight 1

Metrics

  • NPV Calculation Accuracy · Target 0.9

Benchmarks

  • EnergyValBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with block_id, reserve_mmboe, brent_usd, rig_day_rate_usd

    Output: JSON object with npv_million_usd, viable, rank

    Execution harness: Python

Evaluation tools

  • Pandas · Data Processing Engine

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Egypt-UAE Joint Offshore Drilling Concession Analytics Engine