Johnson Richard J, Folwell Benjamin D, Wirekoh Alexander, Frenzel Max, Skovhus Torben Lund
Oil Plus Ltd., Dominion House, Kennet Side, Newbury, Berkshire, RG14 5PX, United Kingdom.
Oil Plus Ltd., Dominion House, Kennet Side, Newbury, Berkshire, RG14 5PX, United Kingdom.
J Biotechnol. 2017 Aug 20;256:57-67. doi: 10.1016/j.jbiotec.2017.04.003. Epub 2017 Apr 8.
Sulphate-reducing prokaryotes (SRP) have been identified in oil field fluids since the 1920s. SRP reduce sulphate to sulphide, a toxic and corrosive species that impacts on operational safety, metallurgy and both capital and operational cost. Differences in water cut, temperature, pressure and fluid chemistry can impact on the observed HS concentration, meaning that an increase in HS concentration does not always correlate with activity of SRP. However it wasn't until the 1990s that SRP activity was accepted as the leading cause of reservoir souring (i.e. an increase in HS concentrations) in water flooded oil fields. The process of sulphate-reduction has been well documented at the genetic, enzymatic and physiological level in pure cultures under laboratory conditions. DNA sequencing has also identified new groups of microorganisms, such as archaea which are capable of contributing to reservoir souring. This has led to some recent advances in microbial control and detection, however, despite this, many of the methods used routinely for microbial control and detection are over a century old. We therefore look towards emerging and novel mitigation technologies that may be used in mitigating against reservoir souring, along with tried and tested methods. Modelling and prediction is another important but often under-used tool in managing microbial reservoir souring. To be truly predictive, models need to take into account not only microbial HS generation but also partitioning and mineral scavenging. The increase in 'big data' available through increased integration of sensors in the digital oil field and the increase in the DNA sequencing capabilities through next-generation sequencing (NGS) therefore offer a unique opportunity to develop and refine microbial reservoir souring models. We therefore review a number of different reservoir souring models and identify how these can be used in the future. With this comprehensive overview of the current and emerging technologies we will highlight areas where significant development effort could generate rewards that can improve detection, prediction and control of microbial reservoir souring.
自20世纪20年代以来,在油田流体中已鉴定出硫酸盐还原原核生物(SRP)。SRP将硫酸盐还原为硫化物,这是一种有毒且具有腐蚀性的物质,会影响操作安全、冶金以及资本和运营成本。含水率、温度、压力和流体化学性质的差异会影响观测到的硫化氢(HS)浓度,这意味着HS浓度的增加并不总是与SRP的活性相关。然而,直到20世纪90年代,SRP活性才被公认为水淹油田储层酸化(即HS浓度增加)的主要原因。在实验室条件下,纯培养物中硫酸盐还原过程在基因、酶和生理水平上已有充分记录。DNA测序还鉴定出了新的微生物群体,如能够导致储层酸化的古菌。这导致了微生物控制和检测方面的一些最新进展,然而,尽管如此,许多常规用于微生物控制和检测的方法已有一个多世纪的历史。因此,我们期待着可用于减轻储层酸化的新兴和新型缓解技术,以及经过试验和测试的方法。建模和预测是管理微生物储层酸化的另一个重要但经常未被充分利用的工具。为了真正具有预测性,模型不仅需要考虑微生物产生HS的情况,还需要考虑其分配和矿物清除。通过数字油田中传感器集成度的提高而获得的“大数据”增加,以及通过下一代测序(NGS)实现的DNA测序能力的提升,因此为开发和完善微生物储层酸化模型提供了独特的机会。因此,我们回顾了一些不同的储层酸化模型,并确定了它们在未来如何使用。通过对当前和新兴技术的全面概述,我们将突出那些通过大量开发工作可能带来回报的领域,这些回报可以改善对微生物储层酸化的检测、预测和控制。