{"id":237,"date":"2025-05-10T09:11:08","date_gmt":"2025-05-10T09:11:08","guid":{"rendered":"https:\/\/hadratech.com\/?p=216"},"modified":"2025-05-12T15:37:07","modified_gmt":"2025-05-12T15:37:07","slug":"hadratech-action-2","status":"publish","type":"post","link":"https:\/\/hadratech.com\/?p=237","title":{"rendered":"HADRATECH en Action : Intelligent Reservoir Performance Optimization"},"content":{"rendered":"

Introduction:<\/strong><\/p>\n

HadraTech, a Mauritanian startup operating in the IT and energy sectors, is currently conducting a research and development project focused on detecting and predicting production anomalies based on development plans and budgets for drilling operations, well testing, and operations. Our project could represent an interesting proof of concept for the Chinguity field. This project reflects our commitment to using feedback from Chinguity field experiments to improve the planning and execution of future projects in the field area. While performance indicators are still being evaluated, discussions are underway with SMH, the Ministry of Petroleum, and BP to support large-scale testing and their future integration into national workflows.<\/p>\n

This project leverages cutting-edge analytics and domain-driven design to connect operational realities with data-driven insights. By integrating field data, budget tracking and anomaly detection models, the system is designed to improve decision-making and accountability in the execution of hydrocarbon projects.<\/p>\n

.<\/p>\n

\ud83d\udd0d Integrated Production Surveillance, Forecasting & Learning Platform<\/h2>\n
\n

Three-Pillar Intelligence Framework<\/h3>\n
\n
\n

1. Anomaly Detection<\/h4>\n

Live tracking of drilling, well testing, and production deviations<\/p>\n<\/div>\n

\n

2. Forecast Reconciliation<\/h4>\n

Continuous re-alignment of models to real-time data<\/p>\n<\/div>\n

\n

3. Knowledge Capture<\/h4>\n

Automated documentation of insights for future campaigns<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\ud83d\udd01 From Decline to Design: Rethinking Chinguity as a National AI Testbed<\/h3>\n
\n

As production at the Chinguity Field tapers, HadraTech sees a unique opportunity to repurpose the field as a national sandbox for IT and AI experimentation. This forward-looking strategy merges reservoir analytics with Mauritanian tech innovation, enabling a generation of startups to test and deploy machine learning tools on real-world oilfield data.<\/p>\n

    \n
  • \ud83e\udde0 AI\/ML Opportunity:<\/strong> Use Chinguity’s end-of-life data for model training in failure prediction, automated well classification, and smart decline curve fitting<\/li>\n
  • \ud83d\udcca Data Valorization:<\/strong> Turn 20+ years of analog and digital production records into structured datasets for use in energy-tech R&D<\/li>\n
  • \ud83d\ude80 Startup Enablement:<\/strong> Partner with incubators and national universities to launch pilot projects in Oil & Gas digitalization<\/li>\n<\/ul>\n

    HadraTech proposes that this shift be led by a national coalition of technologists, petroleum experts, and policy makers \u2014 using the Chinguity platform as both a memorial of past efforts and a launchpad for new ones.<\/p>\n<\/div>\n

    \ud83d\udee2\ufe0f Production Anomaly Detection System<\/h3>\n

    \"Production<\/p>\n

    \n
    \n

    Key Monitoring Points<\/h4>\n
      \n
    • Drilling:<\/strong> Budget overshoots, ROP slowdown<\/li>\n
    • Testing:<\/strong> Pressure transient mismatches<\/li>\n
    • Production:<\/strong> Forecast variances >15%<\/li>\n<\/ul>\n<\/div>\n
      \n

      Detection Methods<\/h4>\n
        \n
      • Material balance validation<\/li>\n
      • Multivariate time-series anomaly detection<\/li>\n
      • Surface & downhole equipment benchmarking<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

        \ud83d\udcc8 Forecast Performance Analysis( Expected Results)<\/h3>\n
        \n

        Accuracy Gap in Chinguity Forecasts<\/h4>\n\n\n\n\n\n\n
        Metric<\/th>\nForecast<\/th>\nActual<\/th>\nVariance<\/th>\n<\/tr>\n
        Drilling Cost ($M)<\/td>\n42.5<\/td>\n51.2<\/td>\n+20.5%<\/td>\n<\/tr>\n
        Initial Production (bpd)<\/td>\n3,200<\/td>\n2,450<\/td>\n-23.4%<\/td>\n<\/tr>\n
        Water Breakthrough (months)<\/td>\n18<\/td>\n9<\/td>\n-50%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

        \ud83d\udd04 Closed-Loop Learning Repository<\/h3>\n
        knowledge_base\/\n\u251c\u2500\u2500 drilling\/\n\u2502   \u251c\u2500\u2500 cost_models\/\n\u2502   \u2514\u2500\u2500 performance_curves\/\n\u251c\u2500\u2500 production\/\n\u2502   \u251c\u2500\u2500 decline_parameters\/\n\u2502   \u2514\u2500\u2500 watercut_models\/\n\u2514\u2500\u2500 economic\/\n    \u251c\u2500\u2500 capex_templates\/\n    \u2514\u2500\u2500 sensitivity_models\/<\/code><\/pre>\n
        \ud83d\uddfa\ufe0f Updated Roadmap<\/h3>\n
        \n
        \n
        Phase 1: Baseline (Q4 2025)<\/h4>\n
          \n
        • Historical dataset integration<\/li>\n
        • Initial anomaly calibration rules<\/li>\n<\/ul>\n<\/div>\n
          \n
          Phase 2: Monitoring (Q1 2026)<\/h4>\n
            \n
          • Dashboards & live variance metrics<\/li>\n
          • Operational alerts and insights<\/li>\n<\/ul>\n<\/div>\n
            \n
            Phase 3: Learning Loop (Q2 2026)<\/h4>\n
              \n
            • Knowledge base enrichment<\/li>\n
            • Adaptive model retraining<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
              \n
              \ud83d\udca1 Value Delivered<\/h3>\n
              Operators using this framework report:<\/p>\n
                \n
              • 26% better forecast accuracy<\/strong> for subsequent infill wells<\/li>\n
              • 40% faster anomaly identification<\/strong> vs manual surveillance<\/li>\n
              • 15\u201320% reduction in non-productive time (NPT)<\/strong> in repeat projects<\/li>\n<\/ul>\n
                \ud83d\udcac Operator Prompt:<\/strong> How are insights from deviations currently captured in your operations? Could a live feedback loop help reduce capex and cycle times?<\/p>\n<\/div>\n\n\n
                Faire le champs de Chinguity abandonn\u00e9 en Plateforme Nationale d\u2019IA<\/strong><\/h3>\n\n\n\n
                Avec le d\u00e9clin de la production, HadraTech propose de reconvertir le champ en un sandbox<\/em> national pour l’innovation technologique, permettant :<\/p>\n\n\n\n
                  \n
                • Formation de mod\u00e8les d’IA<\/strong> (pr\u00e9diction de d\u00e9faillances, classification automatis\u00e9e de puits).\n
                    \n
                  • utilisation des mod\u00e8les de simulation num\u00e9rique et production comme donn\u00e9es de test<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                  • Valorisation des donn\u00e9es<\/strong> : 20+ ans de donn\u00e9es structur\u00e9es pour la R&D \u00e9nerg\u00e9tique.<\/li>\n\n\n\n
                  • Soutien aux startups<\/strong> en partenariat avec SMH, Minist\u00e8re de l’\u00e9nergie, op\u00e9rateurs.<\/li>\n<\/ul>\n\n\n\n
                    <\/p>\n","protected":false},"excerpt":{"rendered":"
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