Sparse Water Fracture Channel Detection from Subsurface Sensors Via a Smart Orthogonal Matching Pursuit

Water front movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the water front movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new smart orthogonal matching pursuit (OMP) algorithm for water front movement detection in carbonate fracture channels. The method utilizes a combined artificial intelligence) AI-OMP approach to first analyze and extract the potential fracture channels and then subsequently deploys a deep learning approach for estimating the water saturation patterns in the fracture channels. The OMP utilizes the sparse fracture to sensor correlation to determine the fracture channels impacting each individual sensor. The deep learning method then utilizes the fracture channel estimates to assess the water front movements. We tested the AI-OMP framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, about 5mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters) and represent an important step towards enhancing reservoir surveillance (Al Shehri, et al. 2021). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fracture channels in conventional and unconventional reservoirs. The system establishes wireless network connectivity via magnetic induction (MI)-based communication, since it exhibits highly reliable and constant channel conditions with sufficiently communication range inside an oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fracture channels to record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation in order to improve sensor measurement data quality, as well as better track the penetrating water fronts. They will move with the injected fluids and distribute themselves in the fracture channels where they start sensing the surrounding environment’s conditions; they communicate the data, including their location coordinates, among each other to finally transmit the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network are transmitted to the control room via aboveground gateway for further processing. The results exhibited strong estimation performance in both accurately determining the fracture channels and the saturation pattern in the subsurface reservoir. The results indicate that the framework performs well; especially for fracture channels that are rather shallow (about 20 m from the wellbore) with significant changes in the saturation levels. This makes the in-situ reservoir sensing a viable permanent reservoir monitoring system for the tracking of fluid fronts, and determination of fracture channels. The novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system of fracture channels flow in carbonate reservoirs. The results outline the capability of in-situ reservoir sensors to deliver accurate tracking water-fronts and fracture channels in order to optimize recovery.

A Physics-Based Proxy for Surface and Subsurface Coupled Simulation Models

Full-physics subsurface simulation models coupled with surface network can be computationally expensive. In this paper, we propose a physics-based subsurface model proxy that significantly reduces the run-time of the coupled model to enable rapid decision-making for reservoir management. In the coupled model the subsurface reservoir simulator generates well inflow performance relationship (IPR) curves which are used by the surface network model to determine well rates that satisfy surface constraints. In the proposed proxy model, the CPU intensive reservoir simulation is replaced with an IPR database constructed from a data pool of one or multiple simulation runs. The IPR database captures well performance that represents subsurface reservoir dynamics. The proxy model can then be used to predict the production performance of new scenarios – for example new drilling sequence – by intelligently looking up the appropriate IPR curves for oil, gas and water phases for each well and solving it with the surface network. All necessary operational events in the surface network and field management logic (such as facility constraints, well conditional shut-in, and group guide rate balancing) for the full-coupled model can be implemented and honored. In the proposed proxy model, while the reservoir simulation component is eliminated for efficiency. The entirety of the surface network model is retained, which offers certain advantages. It is particularly suitable for investigating the impact of different surface operations, such as maintenance schedule and production routing changes, with the aim of minimizing production capacity off-line due to maintenance. Replacing the computationally intensive subsurface simulation with the appropriate IPR significantly improves the run time of the coupled model while preserving the essential physics of the reservoir. The accuracy depends on the difference between the scenarios that the proxy is trained on and the scenarios being evaluated. Initial testing with a complex reservoir with more than 300 wells showed the accuracy of the proxy model to be more than 95%. The computation speedup could be an order of magnitude, depending largely on complexity of the surface network model. Prior work exists in the literature that uses decline curves to replicate subsurface model performance. The use of the multi-phase IPR database and the intelligent lookup mechanism in the proposed method allows it to be more accurate and flexible in handling complexities such as multi-phase flow and interference in the surface network.

Utilization of Digital Mapping and Outcrop Model to Assess Reservoir Characterization and Quality Index: Study Case from Ngrayong Formation in the Randugunting Block, East Java

Digital mapping and digital outcrop modeling are the current state of the art in geology that incorporated traditional field geological mapping and digital technique. Integration of these two techniques produce a realistic earth model that could help to understand its petroleum prospectivity. Our study aims to provide a workflow and illustrate preliminary reservoir characterization as well as reservoir quality index assessment from mapping and digital outcrop model in the area with lack of subsurface dataset, such as limited well and seismic data distribution due to an early exploration stage. We utilized a mix siliciclastic-carbonate outcrop of Ngrayong formation in the Randugunting Block, East Java, collecting several measuring sections and followed by rock sampling per certain interval. Drone acquisition was implemented in the area of interest to generate high resolution 3D outcrop model. The measuring section later on tied with digital outcrop model, producing structural and stratigraphic model. In addition, subsurface reservoir parameters from well and seismic were integrated in order to add accuracy value to our model from surface perspective. Facies and properties model were populated and Reservoir Quality Index (RQI) was calculated to suggest any potential flow units. Our results show excellent alignment between structural, facies, properties model and reservoir quality index to illustrate characteristic of reservoir in the study area. Uncertainty comes from geostatistical approach, data acquisition quality, and theoritical assumption where multiple sensitivity analyses were conducted to optimize the model. Methodology presented in this study can help to assess the reservoir characterization and quality index in the early exploration stage. Thus, reservoir distribution, potential flow units and petroleum prospectivity will be mostly predictable. In addition, this study has successfully visualized and manifested preliminary 3D subsurface reservoir characterization in area with lack of subsurface dataset and reduced significant capital expenditure cost (CAPEX) for acquisition new data on early exploration phase.

Upscaling of outcrop information for improved reservoir modelling – exemplified by a case study on chalk

Outcrops are valuable for analogous subsurface reservoirs in supplying knowledge of fine-scale spatial heterogeneity pattern and stratification types, which are difficult to obtain from subsurface reservoir cores, well logs or seismic data. For petrophysical properties in a domain where the variations are relatively continuous and not dominated by abrupt contrasts, the spatial heterogeneity pattern can be characterized by a semivariogram model. The outcrop information therefore has the potential to constrain the semivariogram for subsurface reservoir modelling, even though it represents different locations and depths, and the petrophysical properties may differ in magnitude or variance. However, the use of outcrop derived spatial correlation information for petrophysical property modelling in practice has been challenged by the scale difference between the small support volume of the property measurements from outcrops and the typically much larger grid cells used in reservoir models. With an example of modelling the porosity of an outcrop chalk unit in eastern Denmark, this paper illustrates how the fine-scale spatial correlation information obtained from sampling of outcrops can be transferred to coarser scale models of analogue rocks. The workflow can be applied to subsurface reservoirs and ultimately improves the representation of geological patterns in reservoir models.

Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity

In marine ecosystems, viruses exert control on the composition and metabolism of microbial communities, influencing overall biogeochemical cycling. Deep sea sediments associated with cold seeps are known to host taxonomically diverse microbial communities, but little is known about viruses infecting these microorganisms. Here, we probed metagenomes from seven geographically diverse cold seeps across global oceans to assess viral diversity, virus–host interaction, and virus-encoded auxiliary metabolic genes (AMGs). Gene-sharing network comparisons with viruses inhabiting other ecosystems reveal that cold seep sediments harbour considerable unexplored viral diversity. Most cold seep viruses display high degrees of endemism with seep fluid flux being one of the main drivers of viral community composition. In silico predictions linked 14.2% of the viruses to microbial host populations with many belonging to poorly understood candidate bacterial and archaeal phyla. Lysis was predicted to be a predominant viral lifestyle based on lineage-specific virus/host abundance ratios. Metabolic predictions of prokaryotic host genomes and viral AMGs suggest that viruses influence microbial hydrocarbon biodegradation at cold seeps, as well as other carbon, sulfur and nitrogen cycling via virus-induced mortality and/or metabolic augmentation. Overall, these findings reveal the global diversity and biogeography of cold seep viruses and indicate how viruses may manipulate seep microbial ecology and biogeochemistry.

Rejuvenating The Concept of The Salodik Group Reservoir of The Banggai Basin Through Surface Geological Mapping And Multi-Method Analyses

Carbonate formations of the Banggai Basin have been proven to be hydrocarbon producers. This research examines Salodik Group properties and provides an analogue to the subsurface reservoir for further development. The methods used in this study are the outcrop samplings at some traverses through fieldwork and laboratory analyses, including petrography, biostratigraphy and SEM. Based on the analyses results and lineament imaging, formation distributions, traverse profiles and cross-sections were generated. Furthermore, facies and reef systems were determined in every formation based on petrographic and biostratigraphic results, by considering organisms, composition, and texture. Based on facies, reef system, and diagenetic environment distribution, a paleogeographic model were interpreted in every age from Middle Eocene to Early Pliocene to represent a better understanding of Salodik Group depositional environment and tectonic events. Through this fieldwork, Salodik Group on the surface was characterized into several equivalent formations in the subsurface, including Lower Tomori Formation, Upper Tomori Formation, Minahaki Formation and Mentawa Member. The formations distribution was greatly influenced by southwest-northeast thrust faults, determined based on lineaments and biostratigraphic analyses, resulting in repetition of the age on some traverses. Formations thickness varies, from approximately 180 to 300 meters. Each formation contains specific facies developed on back to off reef, and depends on organism and texture found through petrographic and biostratigraphic analyses. SEM analysis shows a series of tectonic events that affected the diagenetic process that developed in every formation and age. Banggai Microcontinent collision and further carbonate exposure that produced intense vuggy porosity were indicated by the meteoric vadose diagenetic process since Upper Tomori developed in Late Oligocene. The diagenetic process has a significant role. It generated significant porosity, including in dolomitic and planktonic facies, and possibly influenced further development in carbonate reservoirs, especially in Salodik Group.

Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity

In marine ecosystems, viruses exert control on the composition and metabolism of microbial communities, thus influencing overall biogeochemical cycling. Deep sea sediments associated with cold seeps are known to host taxonomically diverse microbial communities, but little is known about viruses infecting these microorganisms. Here, we probed metagenomes from seven geographically diverse cold seeps across global oceans, to assess viral diversity, virus-host interaction, and virus-encoded auxiliary metabolic genes (AMGs). Gene-sharing network comparisons with viruses inhabiting other ecosystems reveal that cold seep sediments harbour considerable unexplored viral diversity. Most cold seep viruses display high degrees of endemism with seep fluid flux being one of the main drivers of viral community composition. In silico predictions linked 14.2% of the viruses to microbial host populations, with many belonging to poorly understood candidate bacterial and archaeal phyla. Lysis was predicted to be a predominant viral lifestyle based on lineage-specific virus/host abundance ratios. Metabolic predictions of prokaryotic host genomes and viral AMGs suggest that viruses influence microbial hydrocarbon biodegradation at cold seeps, as well as other carbon, sulfur and nitrogen cycling via virus-induced mortality and/or metabolic augmentation. Overall, these findings reveal the global diversity and biogeography of cold seep viruses and indicate how viruses may manipulate seep microbial ecology and biogeochemistry.