مانا بترو
PR-02متوسطةالموجة 2الذكاء الاصطناعيالمجال

التشخيص الذكي لمشكلات الآبار

في جملة واحدة

When a well drops, it names the likely cause — scale, water breakthrough, lift failure, choke or reservoir — instead of only flagging the drop.

المشكلة

A production engineer covering three hundred wells sees a rate drop and has to open five systems to work out why. Most drops are diagnosed days late, and a share of them are never diagnosed at all — the well is simply carried at the lower rate.

كيف تعمل

Rate, pressure, temperature, water cut and lift telemetry are combined into a diagnostic signature, matched against a library of failure patterns built from the operator’s own workover history. The output is a ranked cause list with the evidence for each, and the engineer’s confirmation feeds back as a training label.

البيانات المطلوبة

  • Daily rates with water cut and GOR
  • Wellhead pressure and temperature, choke position
  • Artificial lift telemetry where installed
  • Workover history with the diagnosed root cause per intervention

ما الذي تحصل عليه

  • Ranked cause hypotheses with supporting evidence per well
  • Recommended diagnostic step — well test, survey, or intervention
  • Field-level pattern report showing recurring failure modes by reservoir

ما لا تستطيع فعله

Diagnosis quality depends entirely on how well past workovers recorded their actual root cause, and most operators record the job performed rather than the cause found. Where that history is thin the system distinguishes reservoir-driven from mechanical decline reliably, and does not resolve further.

كل صفحة قدرة تذكر حداً حقيقياً. إن لم يستطع المورّد إخبارك أين يتوقف منتجه، فلم يُقل لك الحق عن أين يبدأ.

قدرات ذات صلة

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