海洋信息问题大体上可以概括为观察、通信与控制 3 大类问题, 本文关注观察与通信, 其理论基 础在于以信息理论与估计理论为核心的信息学. 信息和物理构成我们存在的世界, 或称之为空间, 观 察和通信问题研究通常是在探索信息-物理-空间的关系. 信息理论 (information theory) 或信息学 (informatics) 诞生于 Shannon 1948 年的一篇论文 "一种 通信的数学理论" [6]. 早期 Wiener 和 Rice 先后将随机过程引入通信研究, Shannon 则首次提出信息 熵的概念, 建立了 "信息即是在有限可能性之间做出选择的结果" 这一基本原则, 解决了数据压缩与 可靠通信的基本限这一核心问题, 他并提出了信息熵 (entropy) 与统计力学中物理熵相对应, 与能量 (energy) 这个物理量相对应有信息力 (force) 和信息势 (Potential).
The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical. More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis. These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.
A new definition of the radius of investigation (ROI) is proposed to overcome the ambiguity present in the results from conventional ROI quantification methods. The term ROI is commonly used to quantify the minimum reservoir size or the distance to a potential boundary evaluated through pressure transient testing. However, the various methods that exist in the literature to quantify ROI provide different answers stemming from varying assumptions and thus often lead to confusion in terms of the appropriate definition to choose. Although the ROI method developed by Van Poolen is well recognized in the industry, there is a debate about its general applicability because it is limited to a constant-rate flow period and is insensitive to flowrate and flow sequence, to gauge resolution or measurement noise level. This contrasts with operational experience, where a higher flowrate, higher gauge precision, and a lower level of measurement noise generates higher quality pressure transient testing data from which reservoir boundaries, or other features, can be identified farther away from the wellbore. In other words, higher flowrates, better gauges, and lower noise levels can lead to larger achievable ROI. We propose a new definition of ROI, that is the detectable ROI for each drawdown or build-up flow period and is derived from the actual pressure derivative response and not from a generic model assumption. By defining a derivative noise envelope, the new method clearly identifies the time when the derivative deviates from an unbounded model due to the presence of a boundary and thus provides an estimate of the detectable ROI for the analyzed period. This method overcomes the limitations of most conventional methods and provides ROI predictions that depend on flowrate and gauge noise while maintaining a consistent result with current pressure transient interpretation.
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