The hydrocarbon prospectivity of the Faeroe–Shetland White Zone, located in the area between the Shetland and Faeroe Islands, was assessed in a regional study that integrated seismic and well interpretations with detailed source-rock geochemistry and predictive basin modelling.The Faeroe Basin formed during a Barremian rifting event followed by subsidence during the Late Cretaceous. The Paleocene began with a period of thermal uplift of basement highs and rapid sedimentation which infilled the submarine topography formed during the Cretaceous, and produced marked overpressuring in the basin. Gradual subsidence continued through the Tertiary except for a significant mid Tertiary inversion event that formed several interesting structures in the basin.New thermal models of basins and a new pressure mechanism for inducing hydrofractures that allow vertical hydrocarbon migration from Jurassic source rocks through Cretaceous mudrocks to Tertiary reservoirs, which we call the ‘whoopee cushion effect’, provide the key controls on the hydrocarbon charge mechanism, timing and petroleum composition.The other crucial elements, source, reservoir, and traps which are present at several stratigraphic levels in the White Zone, are summarized in this paper.The interplay of overpressure, hydrocarbon generation and migration during a complex basin evolution makes the White Zone a highly prospective frontier petroleum province.
Existing ownership type systems require objects to have precisely one primary owner, organizing the heap into an ownership tree. Unfortunately, a tree structure is too restrictive for many programs, and prevents many common design patterns where multiple objects interact. Multiple Ownership is an ownership type system where objects can have more than one owner, and the resulting ownership structure forms a DAG. We give a straightforward model for multiple ownership, focusing in particular on how multiple ownership can support a powerful effects system that determines when two computations interfere-in spite of the DAG structure. We present a core programming language MOJO, Multiple ownership for Java-like Objects, including a type and effects system, and soundness proof. In comparison to other systems, MOJO imposes absolutely no restrictions on pointers, modifications or programs' structure, but in spite of this, MOJO's effects can be used to reason about or describe programs' behaviour.
Hybrid Transactional and Analytical Processing (HTAP) databases require processing transactional and analytical queries in isolation to remove the interference between them. To achieve this, it is necessary to maintain different replicas of data specified for the two types of queries. However, it is challenging to provide a consistent view for distributed replicas within a storage system, where analytical requests can efficiently read consistent and fresh data from transactional workloads at scale and with high availability. To meet this challenge, we propose extending replicated state machine-based consensus algorithms to provide consistent replicas for HTAP workloads. Based on this novel idea, we present a Raft-based HTAP database: TiDB. In the database, we design a multi-Raft storage system which consists of a row store and a column store. The row store is built based on the Raft algorithm. It is scalable to materialize updates from transactional requests with high availability. In particular, it asynchronously replicates Raft logs to learners which transform row format to column format for tuples, forming a real-time updatable column store. This column store allows analytical queries to efficiently read fresh and consistent data with strong isolation from transactions on the row store. Based on this storage system, we build an SQL engine to process large-scale distributed transactions and expensive analytical queries. The SQL engine optimally accesses row-format and column-format replicas of data. We also include a powerful analysis engine, TiSpark, to help TiDB connect to the Hadoop ecosystem. Comprehensive experiments show that TiDB achieves isolated high performance under CH-benCHmark, a benchmark focusing on HTAP workloads.
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