A Deep Learning Wag Injection Method for Co2 Recovery Optimization

摘要

Abstract CO2 has some critical technical and economic reasons for its use as an injection gas for oil recovery. CO2 is very soluble in crude oil at reservoir pressures; it contributes to sweep efficiency enhancement as it swells the oil and significantly reduces its viscosity. Although the mechanism of CO2 flooding is the same as that for other gases, CO2 is easier to handle, it is cheaper, and it is an environmentally better candidate than other gases. Formation evaluation and reservoir engineering have been major areas in the oil and gas industry that are heavily influenced by technology advances, to increase efficiency, improve hydrocarbon recovery and allow real-time reservoir monitoring. Water flooding for increasing oil recovery has been amongst the oldest production mechanisms widely utilized since the end of the 19th century to maintain pressure levels in the reservoir and push hydrocarbons accumulations towards the producing wellbore locations (Satter, Iqbal, & Buchwalter, 2008). Produced water from the reservoir formation was reinjected in order to maintain pressure levels, as well as seawater and aquifer water injection have also taken a strong mandate. With the advent of technology and processing plants this injection process was further refined, allowing salinity control of the injected water as well as monitor the injection and distribution of the water levels in near real time (Boussa, Bencherif, Hamza, & Khodja, 2005). Formation evaluation has seen an even greater penetration of technology in its area with the quest to achieve real-time formation evaluation during the drilling process. Conventional formation evaluation is conducted utilizing wireline logging technology, which is deployed after the drilling of the well and allows to analyze the reservoir formation. Given the significant advancement of logging technologies, acquiring the measurements during the drilling process (LWD) has been at the forefront of interest, allowing improved well placement and geosteering as well as real-time formation evaluation to optimize well completion strategies (Hill, 2017). Amongst the technologies recently deployed, surfaced logging and advanced mud and logging allow to determine on cuttings in real time mostly any of the properties previously possible only on direct measurements on cores (Santarelli, Marsala, Brignoli, Rossi, & Bona, 1998; Katterbauer & Marsala, A Novel Sparsity Deploying Reinforcement Deep Learning Algorithm for Saturation Mapping of Oil and Gas Reservoirs, 2021; Katterbauer, Marsala, Schoepf, & Donzier, 2021). With advances in AI, reservoir characterization is now moving towards real-time or near real-time analysis at the rig site. For near real-time analysis, the main physical source of data is drill cuttings as it guides the drilling operation by determining important depth point such as formation tops, coring intervals. Traditionally, the description of these cuttings is done manually by geologists at the well site. The accuracy of these descriptions can be variable depending on the geologist's experience and indeed their mental state and tiredness level. Cores is another source of data. New techniques and older techniques imbued with AI components new allow for greater automation, efficiency, and consistency. The use of AI on traditional images are of great interest in the oil and gas community as they are: 1) fast to acquire, and 2) do not typically require expensive hardware. For example, Arnesen and Wade used convolutional neural networks; specifically, an inception-v3 inspired architecture, to predict lithological variations in cuttings (Arnesen & Wade, 2018). In their study, each sample is related to one lithology. Buscombe used a customized convolutional neural network to predict the granulometry of sediments, specifically the grain size distribution (Buscombe, 2019). Similarly, automated core description systems (e.g., (Kanagandran; de Lima, Bonar, Coronado, Marfurt, &