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Transcend conventions to optimize CAPEX and OPEX

机译:超越惯例以优化CAPEX和OPEX

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The oil & gas (O&G) industry is in a state of flux, fueled by unpredictable changes in commodity price, rapid technology advances, changing customer needs, stringent environmental regulations and aging plants. This lack of predictability impacts resource utilization and calls for a measured focus, including as follows: 1. Disciplined capital investment 2. Maximization of asset delivery, from exploration to refineries 3. Streamlined, well-defined personnel roles. While cash flows have looked more promising, project scopes have expanded and bring bigger challenges to curbing operational costs and managing project risks. Given the industry's propensity for mega-projects that leverage economies of scale, it's time to align the humongous capital project cost with demands to shorten exploration & production (E&P) cycles. The industry has achieved substantial material cost reductions. As a next step, according to PwC and others, digital technology implementation cumulatively can save up to $100 billion in the upstream sector by 2025. An immediate focus should be implementing a robust enterprise data management (EDM) platform for improved collaboration and asset data management (ADM) strategies to drive operational efficiency. The O&G industry is awash with more data than it can manage. Companies access diverse data sets generated by smart sensors, logging tools, geophones and other sources. Analytics layer insights on top of this data deluge, enabling better decision-making. As analytics-driven operating models become the norm, the focus will shift toward data quality.
机译:石油和天然气(O&G)行业处于瞬息万变的状态,商品价格发生不可预测的变化,技术进步日新月异,客户需求不断变化,严格的环境法规以及工厂老化,这助长了这一趋势。缺乏可预测性会影响资源利用,并需要重点关注以下方面:1.有纪律的资本投资2.从勘探到精炼的资产交付最大化3.精简的,明确的人员角色。尽管现金流看起来更有希望,但项目范围已经扩大,在控制运营成本和管理项目风险方面带来了更大的挑战。鉴于行业倾向于利用规模经济的大型项目,是时候调整庞大的资本项目成本与缩短勘探与生产(E&P)周期的要求了。该行业已大大降低了材料成本。普华永道等人认为,下一步,到2025年,数字技术的实施累计可为上游行业节省多达1000亿美元。当前的重点应该是实施强大的企业数据管理(EDM)平台,以改善协作和资产数据管理(ADM)策略来提高运营效率。 O&G行业充斥着无法管理的更多数据。公司可以访问由智能传感器,测井工具,地震检波器和其他来源生成的各种数据集。在此数据泛滥的基础上,Analytics层提供了见解,可以做出更好的决策。随着分析驱动的运营模型成为规范,重点将转向数据质量。

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