مانا بترو
DR-08متوسطةالموجة 2الذكاء الاصطناعي

التنبؤ بجودة أعمال الأسمنت

في جملة واحدة

Predicts whether the cement behind the casing will isolate properly, from the job data, before the evaluation log is run.

المشكلة

A poor primary cement job is usually discovered weeks later through a bond log or, worse, through sustained casing pressure years later. Remedial squeezes cost far more than getting the first job right.

The job data — pump rate, density, returns, displacement efficiency, centraliser placement — contains most of the signal, and is normally archived without analysis.

كيف تعمل

A gradient-boosted classifier is trained on historical jobs labelled by their cement bond and variable density log interpretation, using job execution data, hole geometry from caliper, centralisation standoff and mud removal indicators. Predictions come with the top contributing factors so the engineer can act on them.

Where a job is predicted poor, the system proposes the specific change — rate, spacer volume, standoff — rather than only flagging risk.

البيانات المطلوبة

  • Cementing job records: rates, pressures, densities, volumes, returns
  • Caliper log for annular volume and standoff calculation
  • Centraliser placement and casing tally
  • Historical CBL/VDL interpretations for training labels

ما الذي تحصل عليه

  • Predicted isolation quality by interval with contributing factors
  • Pre-job recommendation on rate, spacer and standoff
  • Field-level trend of job quality by rig, crew and service provider

ما لا تستطيع فعله

The training labels come from bond-log interpretation, which is itself subjective and varies between interpreters and between tool vendors. The model inherits that noise. It is reliable at separating clearly good from clearly bad jobs and is not trustworthy in the middle band, which we report explicitly rather than forcing a binary call.

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

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