Natural gas is sampled and produced throughout the lifespan of a petroleum field. Gas composition and isotope data are critical inputs in the exploration and field development, such as gas show identification, petroleum system analysis, fluid characterization, and production monitoring. On-site gas analysis is usually conducted within a mud gas unit, which is operationally unavailable after drilling. Gas samples need to be taken from the field and shipped back to the laboratory for gas chromatography and isotoperatio mass spectrometry analyses. Results are usually without sufficient resolution to fully characterize the heterogeneity and dynamics of fluids within the reservoir and the production system. In addition, it often takes a considerable time to obtain the results using the traditional method. A novel QEPAS (quartz-enhanced photoacoustic spectroscopy) sensor system was developed to move gas composition analyses to field for quasi-real-time characterization and monitoring. With respect to previously reported QEPAS prototypes for trace gas detection, the new system realized measuring concentrations of methane (C1), ethane (C2), and propane (C3) in gas phase within the percentage range that is typically encountered in natural gas samples from oil and gas fields. A gas mixing enclosure was used to dilute the natural gas-like mixtures in nitrogen gas (N 2 ) to avoid the saturation of QEPAS signals. An iterative analysis based on multilinear regression of QEPAS spectra was developed to filter out the influence of gas matrix variation from multiple hydrocarbon components. The advance in simultaneous measuring hydrocarbon gases and expanded linearity range of QEPAS, with previously reported detection of H 2 S, CO 2 , and gas isotopes ( 12 CO 2 / 13 CO 2 , 13 CH 4 / 12 CH 4 ), opens a way to use the advanced sensing technology for in situ and real-time gas detection and chemical analysis in the oil industry.
Reservoir management practices are classically based on analytical models and standard Reservoir Engineering tools. In the waterfood or water alternating gas recovery process, the analysis is made traditionally with the hypothesis of constant predefined patterns. The producer – injector pair's interaction is quantified based on predefined geometrical analysis of the percentages of contribution of each injector to a producer. In the absence of certain degree of reservoir homogeneity, and also possible injection/production technical issues this method presents a lot of limitation and may lead to erroneous results. Fields in the Middle East are dominantly carbonates and the flow paths are guided by heterogeneous distribution of reservoir characteristics mainly permeability. This paper outlines a case study for the usage of streamline simulation in predefining the optimized rates of each producer and injector in order to optimize the recovery from individual pattern. The study quantified the interaction between producers and injectors pairs and defined the dynamic pattern distribution through the history. A number of attributes can be derived for each producer injector and pattern. Attributes such as the instantaneous and cumulative voidage replacement ratio, sweep efficiency and injection leakage can be analyzed in order to give more weight in the optimization stage to certain producer and certain injectors. It was concluded that the geometrical lay out of the patterns is not necessary respected and the injectors may support producers outside their geometrical patterns. There was as well a certain amount of the injection that is not contributing to any production and it is not targeting or supporting any specific well. A number of forecast scenarios were conducted and through ranking different realizations based on total patterns sweep efficiency, the best scenario was selected to determine the allowable volumes to be injected and produced. The scenario showed better control of the patterns as there was a reduction of any redundant injection and the leakage was cut down.
Summary Prediction of well production from unconventional reservoirs is often a complex problem with an incomplete understanding of physics and a considerable amount of data. The most effective way for dealing with it is to use the gray-box approach that combines the strengths of physics-based models and machine learning (ML) used for dealing with certain components of the prediction where physical understanding is poor or difficult. However, the development of methodologies for the incorporation of physics into ML is still in its infancy, not only in the oil and gas industry, but also in other scientific and engineering communities, including the physics community. To set the stage for further advancing the use of combining physics-based models with ML for predicting well production, in this paper we present a brief review of the current developments in this area in the industry, including ML representation of numerical simulation results, determination of parameters for decline curve analysis (DCA) models with ML, physics-informed ML (PIML) that provides an efficient and gridless method for solving differential equations and for discovering governing equations from observations, and physics-constrained ML (PCML) that directly embeds a physics-based model into a neural network. The advantages and potential limitations of the methods are discussed. The future research directions in this area include, but are not limited to, further developing and refining methodologies, including algorithm development, to directly embed physics-based models into ML; exploring the usefulness of PIML for reservoir simulations; and adapting the new developments of how the physics and ML are incorporated in other communities to the well-production prediction. Finally, the methodologies we discuss in the paper can be generally applied to conventional reservoirs as well, although the focus here is on unconventional reservoirs.
Hydraulically fracturing long horizontal wells is the key technology for economically producing hydrocarbon from unconventional reservoirs. A reservoir's fracability (the ease by which it can be hydraulically fractured) has often been used as an important parameter for identifying the sweet spots for production. Several fracability indicators, based on different types of rock properties, including mechanical, geochemical, and mineralogical properties, have been developed and used in industry. This study, based on observations from a source rock reservoir, proposes the use of reservoir water saturation as a new fracability indicator for organic‐rich tight carbonate source rocks that are not clay rich. The results from a machine learning model trained with the observations clearly show the strong and positive correlation between the linear flow parameter (that is obtained based on the newly proposed equivalent‐state approximation and characterizes the effectiveness of hydraulic fracturing) and the water saturation for oil wells, but not for gas wells. While further investigation is needed, the results may be due to the dual wettability of the carbonate source rock. Since minerals are more water‐wet than the organic matter, reservoir water tends to occupy pore spaces in the mineral matrix. Thus, water saturation reflects the relative portion of mineral matrix pore spaces. Given that the low‐clay content mineral matrix contains all the brittle components, the pore‐space development in the mineral matrix may have important implications for the fracability in the hydraulic fracturing process.
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