Flow diagnostics for (fractured) geothermal reservoirs

静态和动态储层建模是预测地质储层资源潜力和流动特性的常用方法。然而，现有的油藏建模工作流程经常在地球科学和工程之间造成障碍。所谓的“流诊断”（FD）可以克服这个问题。FD使用诸如飞行时间等直观的度量来近似和量化储层动态（图1）。SINTEF的开创性工作展示了如何在几秒钟内计算出FD，并在建立和更改地质模型时提供有关储层动态的实时反馈。这使得地球科学家和工程师可以在开始进一步的油藏研究之前共同探索地质中的不确定性如何影响流动行为。FD还可以快速筛选大型油藏模型集合，以选择较小的模型子集进行额外的研究，而不会影响原始集合中捕获的地质不确定性（图2）。因此，FD适用于分析地下数据有限且地质不确定性高的复杂情况下的储层行为，例如（裂缝）地热储层。因此，FD可以在描述和开发地热储层时做出基于价值的决策，并有助于降低风险。德国地热储层开发的实例表明，获取正确的额外地质数据（如三维地震）对于限制不确定性和降低风险（如干井）非常有价值，这些数据会对公众接受度和项目经济产生不利影响。该博士项目旨在将原本用于（裂缝性）油气储层的FD应用于地热储层。项目第一年将专注于开发和实施一种新的FD方法，使用导师开发的现有数学理论来模拟热锋面的运动、地质力学效应和裂缝-基质相互作用。第二年将侧重于概念验证研究，展示如何使用新的FD技术来筛选代表半合成地热储层的集合，以选择合适的储层模型子集，而不会影响地质不确定性下预测和优化储层性能的能力。第三年级将侧重于实际应用，展示如何使用FD加速和改进在康沃尔联合唐斯深层地热项目中由nerc资助的GWatt项目进行的储层建模和模拟研究。

Static and dynamic reservoir modelling are common to predict resource potentials and flow behaviours in geological reservoirs. However, existing reservoir modelling workflows often create barriers between geoscience and engineering. So called "flow diagnostics" (FD) can overcome this problem. FD approximate and quantify the reservoir dynamics using intuitive measures such as the time-of-flight (Fig. 1). The seminal work at SINTEF demonstrated how FD can be computed in the matter of seconds, providing real-time feedback on reservoir dynamics while the geological model is built and changed. This allows geoscientists and engineers to jointly explore how uncertainties in geology impact flow behaviours before commencing further reservoir studies. FD also enable rapid screening of a large ensemble of reservoir models to select a smaller subset of models for additional studies without compromising on the geological uncertainties that have been captured in the original ensemble (Fig. 2). FD are hence suited to analyse reservoir behaviours in challenging situations where subsurface data is limited and geological uncertainty is high, such as (fractured) geothermal reservoirs. FD therefore enable value-based decisions when characterising and developing geothermal reservoir and help to mitigate risks. Examples from developing geothermal reservoirs in Germany have shown that acquiring the right additional geological data (e.g. 3D seismics) is very valuable to constrain uncertainties and reduce risks (e.g. dry wells) that have adverse impact on public acceptance and project economics. This PhD project aims to adapt FD that were originally developed for (fractured) hydrocarbon reservoirs to geothermal reservoirs. Year 1 of the project will focus on developing and implementing a new FD methodology, using existing mathematical theories developed by the supervisors to model the movement of thermal fronts, geomechanical effects, and fracture-matrix interactions. Year 2 will focus on a proof-of-concept study that demonstrates how the new FD technology can be used to screen an ensemble representing a semi-synthetic geothermal reservoir to select an appropriate subset of reservoir models without compromising on the ability to forecast and optimise reservoir performance under geological uncertainty. Year 3 will focus on a real-field application, showcasing how the reservoir modelling and simulation studies that are carried out under the NERC-funded GWatt project at the United Downs Deep Geothermal Project in Cornwall can be accelerated and improved using FD.