Against the backdrop of global oil and gas exploration and development continuously advancing into deepwater, deep formation and unconventional complex domains, the inherent flaws of conventional oil & gas digitalization and geological modeling have been fully exposed, trapping the industry’s intelligent transformation in a bottleneck. GridWorld has dedicated itself to the vertical oil and gas geology sector. Relying on its self-developed generative AI geological foundation model tEgg, the company launches an integrated digital twin solution covering multi-scale exploration and development spanning basins, blocks and reservoirs. Leveraging geology-native AI capabilities, GridWorld reshapes a new intelligent operation paradigm for upstream oil and gas exploration and development.

Industry Dilemmas: Five Core Pain Points Choking Oil & Gas Intelligent Transformation

Most mainstream industrial digitalization solutions are shallow informatization systems developed by general IT vendors, which fail to adapt to professional subsurface geological operations. The key pain points are as follows:

1.Segregation across three geological scales: basin, block and reservoir models lack mutual constraints and interlinkage;

2.Static and rigid geological models: manual remodeling takes months, unable to adapt to new data generated while drilling and production;

3.Disconnected closed-loop iteration of data, models, business workflows and model training, hindering full exploitation of data value;

4.Fragmented exploration and development workflows, leading to redundant modeling and inefficient cross-disciplinary collaboration;

5.Generic AI models unbound by geological mechanisms, prone to frequent geological hallucinations that render outputs unfit for drilling and production decision-making.

Core Solution: Five-Tier Closed-Loop Architecture Enabling Full-Scale Cross-Level Modeling

Built around the geology-native tEgg foundation model, a proprietary five-tier closed-loop architecture is deployed to realize perceivable, modelable, evolvable and applicable subsurface geological bodies. The framework vertically integrates five layers: multi-source data access, exclusive geological knowledge graph, core AI engine, multi-scale digital twins, and integrated business applications. It establishes a three-level nested system featuring macro basin constraints, mesoscale block characterization and microscale reservoir simulation. Large-scale frameworks constrain small-scale models, while small-scale results reversely calibrate large-scale frameworks, fully unifying geological logic across all scales. Furthermore, a full-link closed loop covering data governance, intelligent modeling, business application and knowledge feedback is constructed to enable autonomous iterative optimization of models.

Core Competitive Advantages: Geology-Native AI, Distinct from Generic Commercial Digitalization Solutions

Differentiated from generic large-model digitalization projects available on the market, this solution boasts exclusive vertical domain competitiveness:

1.Deep integration with oil and gas geological mechanisms: AI algorithms align with professional laws of sedimentology, structural geology and fluid seepage, fundamentally eliminating geological hallucinations;

2.Exclusive cross-scale automatic evolution capability: newly added data drives incremental model updates, creating self-evolving living geological digital twins that gain precision with continuous use;

3.Support for on-premises private deployment with full data sovereignty control, complying with industrial data governance and compliance requirements;

4.Direct targeting of core production capacity workflows including prospect screening, geosteering while drilling and remaining oil tapping, rather than superficial equipment maintenance or visual dashboards, delivering strong practical business implementation value.

Implementation Roadmap: Low-Risk Phased Rollout with Quantifiable Benefits

A lightweight phased deployment model is adopted to cut enterprises’ trial-and-error costs during transformation:

1.Complete POC technical verification within 4 weeks to validate core AI modeling capabilities;

2.Launch single-block pilot deployment in 3–4 months, rolling out core functions including dynamic modeling, drilling risk control and development optimization;

3.Achieve enterprise-wide large-scale deployment within six months to build an exclusive enterprise geological AI platform.

Post-implementation outcomes include effective reduction of dry well rates and non-productive drilling time, alongside a 5%–8% uplift in block recovery factor, delivering multi-dimensional value covering risk mitigation, cost reduction, reserve expansion and recovery enhancement.

From static geological models to self-evolving living digital twins; from fragmented digitalization to integrated cross-disciplinary collaboration across the full exploration and development lifecycle. Centered on the generative AI geological foundation model tEgg, GridWorld maintains deep vertical expertise focused on core oil and gas businesses. It addresses critical challenges in complex oil and gas reservoir development, empowers oil and gas enterprises to build proprietary geological digital assets, and fuels long-term intelligent upgrading of upstream oil and gas operations.