Fractures can be first-order controls on fluid flow in hydrocarbon reservoirs. Understanding the characteristics of fractures such as their aperture, density, distribution, conductivity, connectivity, etc, is key for reservoir engineering and production analysis. Well testing plays a key role in the the characterisation of fractured reservoirs, especially. New advances in the Pressure Transient Analysis (PTA) have enabled the interpretation of production data in a way where the resulting geological scenarios are in better agreement with fracture patterns observed in outcrop analogues. Traditionally, Drill Stem Test (DST) data have been the primay source of information for well testing. However, we hypothesise that wireline conveyed tools designed for Interval Pressure Transient Testing (IPTT) could yield a more throrough description of the near-wellbore heterogeneities, including fractures. Hence, we investigate the applicability of IPTT for characterising fractured reservoirs using detailed numerical simulations models with accurate wellbore representation to generate synthetic IPTT responses that can obtained through a next-generation wireline testing tool called SATURN. We particularly focus on cases where fractures are present in the near-wellbore region but do not intersect the wellbore. The study included parameters such as fracture densities and conductivities, distance between fractures and wellbore and the vertical extension of the fractures across geological beds. The impact of the different fracture scenarios on the pressure transient tests was recorded as characteristic signatures on diagnostic plots (pressure derivative curves). We have called these curves "IPTT-Geotypes"; they can be used to assist the interpretation process of IPTT responses. To the best of our knowledge, this is the first time pressure derivative type curves for IPTT in fractured reservoirs are presented in the literature. A field example of an IPTT case was analysed using the concept of geological well testing. We integrated the information from petrophysical logs and the IPTT-Geotypes to assist the calibration of a reservoir model developed to represent the geological setting of the tested reservoir interval. The results provided a sound interpretation of the reservoir geology and quantitative estimation of the matrix and fracture parameters.

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is particularly challenging in the presence of fractures because they can manifest themselves in many different p′ curves. In this work, we aim to provide a proof-of-concept machine learning approach that allows us to effectively handle the diversity in fracture-related p′ curves by automatically classifying them and identifying the characteristic fracture patterns. We created a synthetic dataset from numerical simulation that comprised 2560 p′ curves that represent a wide range of fracture network properties. We developed an unsupervised machine learning approach that can distinguish the temporal variations in the p′ curves by combining dynamic time warping with k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the p′ curves if the second pressure derivatives are used as the classification variable. Our analysis indicated that 12 clusters were appropriate to describe the full collection of p′ curves in this particular dataset. The classification exercise also allowed us to identify the key geological features that influence the p′ curves in this particular dataset, namely (1) the distance from the wellbore to the closest fracture(s), (2) the local/global fracture connectivity, and (3) the local/global fracture intensity. With additional training data to account for a broader range of fracture network properties, the proposed classification method could be expanded to other naturally fractured reservoirs and eventually serve as an interpretation framework for understanding how complex fracture network properties impact pressure transient behaviour.

The sector screening review is a surveillance tool used to assess and find opportunities to increase the oil production and improve the performance of the reservoir. We developed a novel interdisciplinary workflow (geology-engineering) integrating dynamic and static data in order to generate opportunities at well and field level; this methodology was used to analyze the impact of fractures in the reservoir performance and management. The complexity of the geology on areas near a graben system (structure at center of the field with biggest vertical displacement) was suspected to cause flow anomalies that ultimately affected the well productivity indexes. After an exhaustive evaluation, it was noticed that a well showed lower productivity index (PI), 2-3 times less than nearby producers in the area, same reservoir Unit Z2 (similar lengths, conditions). To understand the root cause of such performance, a geoengineering workflow was implemented, integrating pressure transient analyses (PTA), production logging (PLT), bottom hole image (BHI), seismic (exceptionally complete dataset) and extrapolated to other wells with similar behavior. The PLT showed that 70% of the well contribution was concentrated in only a small interval of the horizontal section, this interval was correlated to a conductive fault through BHI, which was also detected by seismic (correlates with low velocity anomaly). The PTA showed unexpected pressure transient behavior suspected to be related to the dynamic effect of the fault and associated fractures. Learnings from above analyses triggered actions in different scales/stages: at Well scale, 1st Stage: the well was selected to be completed using selective stimulation with abrasive jet, to remove damage of the first 400 ft. of the well (skin factor masked by fracture contribution) and unlock the potential of non-contributing zone (after fault, to toe); allowing the well to produce 25% additional oil and doubling the PI. 2nd Stage (planned): workover proposal to install lower completion (LC), to ensure even depletion, avoid by-passed oil and prevent early water/gas breakthrough. Field scale: new wells to be drilled in reservoir zones potentially affected by the graben will be equipped with LC. Finally, a geological well testing framework matching the PBU and PLT was implemented based on a high resolution geological model designed to capture the properties of the matrix and fractures. The results from this study were used as diagnostic tool for additional wells with similar conditions which lack PLT data. Noticeably, the presence of flow controlling fractures was usually suspected but not properly assessed/quantified in this reservoir, mainly due to the fact that the dynamic impact of these fractures was masked by the overlapping of different geological phenomena. The implementation of our geological-engineering workflow allowed immediately triggering actions that could lead to major performance enhancements at field- and well-level, including field development, management and modelling practices in such complex geological arquitectures.

No abstract

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure Transient Analysis (PTA) could play a key role for fracture characterization purposes if proper links are built between the pressure derivative responses () and the fracture properties; however, the PTA process is particularly challenging in the presence of fractures because they can manifest themselves in many different . Our work aims at providing a tool for effectively handling the diversity in fracture-induced by automatically classifying them and identifying characteristic patterns in the data. To represent this diversity of in naturally fractured reservoirs, we created a synthetic dataset from numerical simulation that comprised 2560 , corresponding to 10 stochastic realizations of 256 combinations of different fracture intensities and average fracture apertures, organized in two orthogonal fracture sets. To group the , we developed an unsupervised maching learning approach that can distinguish beyond the variable temporality of the signals by leveraging the dynamic time warping algorithm in a k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the first pressure derivatives if the second pressure derivatives are used as the classification variable. The analysis of the Naturally Fracture Reservoirs dataset indicated that 12 clusters were appropriate to describe the full collection of . We suggest that the respective cluster medoids can be regarded as reference curves for various Naturally Fractured Reservoirs and be used as a guide in the interpretation of real well tests. The classification exercise also allowed us to identify the key geological features that influence the , namely 1) the distance from the wellbore to the closest fracture(s), 2) the local/global fracture connectivity, and 3) the local/global fracture intensity.