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.

Artificial Intelligence Aided Proxy Model for Water Front Tracking in Fractured Carbonate Reservoirs

Saturation mapping in fractured carbonate reservoirs is a major challenge for oil and gas companies. The fracture channels within the reservoir are the primary water conductors that shape water front patterns and cause uneven sweep efficiency. Flow simulation for fractured reservoirs is typically time-consuming due to the inherent high nonlinearity. A data-driven approach to capture the main flow patterns is quintessential for efficient optimization of reservoir performance and uncertainty quantification. We employ an artificial intelligence (AI) aided proxy modeling framework for waterfront tracking in complex fractured carbonate reservoirs. The framework utilizes deep neural networks and reduced-order modeling to achieve an efficient representation of the reservoir dynamics to track and determine the fluid flow patterns within the fracture network. The AI-proxy model is examined on a synthetic two-dimensional (2D) fractured carbonate reservoir model. Training dataset including saturation and pressure maps at a series of time steps is generated using a dual-porosity dual-permeability (DPDP) model. Experimental results indicate a robust performance of the AI-aided proxy model, which successfully reproduce the key flow patterns within the reservoir and achieve orders of shorter running time than the full-order reservoir simulation. This suggests the great potential of utilizing the AI-aided proxy model for heavy-simulation-based reservoir applications such as history matching, production optimization, and uncertainty assessment.

Analisis pendapatan pedagang kaki lima di kawasan wisata Water Front City Kecamatan Tungkal Ilir Kabupaten Tanjung Jabung Barat

This study aims to analyze the socio-economic characteristics of street vendors and analyze the income of street vendors in the Water Front City tourist area, Tungkal Ilir District, Tanjung Jabung Barat Regency. The data used in this study are primary data obtained using field research sourced from street vendors in the Water Front City tourist area as a sample. The sampling method used in this study is Stratified Random Sampling. The data were analyzed using descriptive qualitative and quantitative descriptive analysis methods. The results of the study found that the socio-economic characteristics of street vendors in the Water Front City tourist area were based on gender, age, education level, number of family members, work experience, and income. Based on the results of data processing, the income of street vendors will increase obtained from the regression coefficient of the venture capital variable of 1.080051 which has a significant effect on the income of street vendors with a probability level below 5% (0.05). While the variable working hours of 68927.75 and length of business of 169676.8 has no significant effect on the income of street vendors in the Water Front City tourist area, Tungkal Ilir District, Tanjung Jabung Barat Regency. Keywords: Income, Socio-economic characteristics of street vendors, Multiple linier regression analysis.

Millimeter scale water movement on convex and concave surfaces of porous media under microgravity.

<p> Human’s sphere of activities is going to expand to Moon and Mars on 2030s. As manned space mission getting longer, the importance of extra-terrestrial agricultural production increase not only for food production, but also for phycological benefit for astronauts. Water movement in porous media must be understood for secured plant growth, previous researches, however, reported that slower capillary flow was observed under microgravity than under Earth gravity (1 G). Air entrapment on pore neck may induce higher tortuosity and made capillary flow slower under microgravity. It was also reported that widening shape on capillary tube restrict water movement in capillary tube under microgravity. The diameter of capillary tube was relatively large (0.8 mm to 2.3 mm in-diameter) in the previous report; therefore, it is unclear that the result is applicable to the smaller pore structure like porous media. The objective of this study is (1) to evaluate capillary flow rate on convex and concave surface on the particle of porous media under microgravity and under 1 G, (2) to evaluate the water movement on widening area made by boundary between 0.8 mm and 1.0 mm glass beads. To make water movement visible, acrylic column of 2 mm thickness was chosen and was filled with 4 cm layer of 0.8 mm diameter glass beads and 3 cm layer of 1.0 mm diameter glass beads. Distilled water dyed with methylene blue solution was infiltrated into the glass beads under 2.4 s microgravity condition induced by 50 m free fall or under 1 G condition. Capillary flow was taken by high speed (960 fps) and closeup camera (DSC-RX100M5A, SONY) and split into image sequences to analyze with software (ImageJ). Both under microgravity and under 1 G, capillary flow stuck on the convex surface and hardly infiltrated into the concave surface, however, once water crossed over the convex surfaces, water moved on concave surfaces very fast. Pore was filled with water and air entrapment on pore neck, predicted on previous research, was not observed. The water front firstly reached on the boundary of 0.8 mm to 1.0 mm glass beads stopped, however, after the surrounding water front catch up, water crossed over the boundary. This result suggested that widening area restricted capillary flow, however it did not shut-off.</p>

Validation of Surge Model Using Furrow Gradient and Flow Retardance

Introduction: Surface irrigation, our oldest method of applying water on to the cropped land, has withstood the test of time because of its many advantages. Over the years, minor changes have been made to improve the efficiency of surface irrigation system. Aim: The present study was taken to validate the existing model with furrow gradient and flow retardance. Principle: The experimental layout has been made to accommodate the variance such as the furrow gradients (0.3%, 0.6% and 0.1%), the modes of irrigation namely the continuous flow as control and the surge flow as the treatment. Surge irrigation is a relatively new technique whereby water to surface irrigated furrows is applied intermittently in a series of relatively short ON and OFF time periods of irrigation cycles. Results: It is claimed that the ON-OFF cycling of the flow for specific time periods produces surges during the ON period and influences the soil intake during the OFF period when water soaks into the soil. The net result is a reduction in soil infiltration rates during subsequent surge ON periods and an increase in the rate of water front advance. The SURGEMODE model can only gives the net water front advance time that can be predicted for non-vegetated condition and a standard reference slope. However when the furrow is getting vegetated or when the slope gradients are changed, the water front advance predicted through the existing model cannot be predict accurately. Conclusion: Hence, the study involved to validate the existing model with furrow gradient and flow retardance. The use of revalidated existing SURGEMODE model with the correction factor would be the exact suitable model for the local condition.