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Abstract Decline curve analysis has been used as a reliable method to forecast conventional reservoir well production over the last decades. Recently, an increase in the demand for oil and gas has caused unconventional reservoirs to become a prominent source of energy. However, it is challenged if we still apply the decline curve analysis in unconventional reservoirs due to its limitations such as boundary dominated flow, constant operation condition, et al. Therefore, in this paper, two new methods are proposed using machine learning method to forecast well production in unconventional reservoirs, especially on the EOR pilot projects. The first method is the Neural Network, which allows the analysis of large quantities of data to discover meaningful patterns and relationships. Both peak production rate and hydraulic fracture parameters are used to be the key factors. Lastly, Neural Network technology is applied to investigate the relationship between key factors and oil production rate. The second method uses the Time Series Analysis. Time Series Analysis is one of the most applied data science techniques in business and finance. Since the properties of unconventional reservoir make the production prediction more difficult, it is safe to say that Time Series Analysis can yield good results on the production rate forecast. Field production data from over 1000 wells from different shale plays (Barnett, Bakken, Bone Springs, Eagle Ford oil, Eagle Ford gas, Fayetteville, Marcellus gas, Marcellus oil, Utica oil, and Woodford) is used to verify the feasibility of these two methods. The results indicate there is a good match between the available and predicated production data. The overall R values of Neural Network and Time Series Analysis are above 0.8, which demonstrates that Neural Network and Time Series Analysis are reliable to study the dataset and provide proper production prediction. Meanwhile, when dealing with the EOR production prediction, such as Huff-n-Puff, Time Series Analysis shows more accurate results than Neural Network. This paper proposes a thorough analysis of the feasibility of machine learning in multiple unconventional reservoirs. Instead of repeatedly fitting the production data by decline curve analysis, it also provides a more robust way and meaning reference for the evaluation of the wells.
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > West Virginia > Appalachian Basin > Utica Shale Formation (0.93)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.93)
- (19 more...)
Abstract The improved oil recovery of unconventional shale reservoirs has attracted much interest in recent years. Gas injection, such as CO2 and natural gas, is one of the most considered techniques for its sweep efficiency and effectiveness in low permeability reservoirs. However, the uncertainties of fluid phase behavior in shale reservoirs pose a great challenge in evaluating the performance of gas injection operation. Shale reservoirs are featured with macro-scale to nano-scale pore size distribution in the porous space. In fractures and macropores, the fluid shows bulk behavior, but in nanopores the phase behavior is significantly altered by the confinement effect. The integrated behavior of reservoir fluids in this complex environment remains uncertain. In this study, we investigate the nano-scale pore size distribution effect on the phase behavior of reservoir fluids in gas injection for shale reservoirs using a multi-scale equation of state modeling. A case of Anadarko Basin shale oil is used. The pore size distribution is discretized as a multi-scale system with pores of specific diameters. The phase equilibria of methane injection into the multi-scale system are calculated. The constant composition expansions are simulated for oil mixed with various fractions of injected gas. Bubble point, swelling factor, criticality and fluid volumetrics are studied in comparison to the behavior of the bulk fluid. It is found that fluid in nanopores becomes supercritical with injected gas, but lowering the pressure below bubble point will turn it into the subcritical state. The swelling factor is slightly higher with nanopores, and bubble point is lower than the bulk. The degree of deviation depends on the amount of injected gas.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Gas-injection methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
Abstract We present the first comprehensive experimental evaluation of gas injection for EOR in organic rich shale. Experiments in preserved core demonstrated the potential of CO2 to extract the naturally occurring oil in organic rich shale reservoirs, whereas tests in re-saturated core plugs were used to compute accurate recovery factors, and evaluate the effect of soak time, operating pressure, and the relevance of slim-tube MMP on recovery. 18 core-flooding experiments were conducted in sidewall cores from different shale plays. The cores re-saturated with crude oil, were first cleaned by Dean-Stark extraction, and submitted to porosity and compressibility determination. The re-saturation, confirmed by CT-scanning, was attained by aging the core plugs at high pressure for two to four months. In all experiments, glass beads surrounding core samples were used to simulate the proppant and physically recreate in the laboratory a hydraulic fracture connected to the shale matrix. The slim-tube MMP was measured with CO2, and core-flooding experiments were performed below, close to, and above the MMP. The displacement equipment was coupled to a medical CT-scanner that enabled us to track the changes in composition and saturation taking place within the shale cores during the experiments. Continuous CO2 injection and huff-and-puff were evaluated using soak time from zero to 22 hours. Fixed reservoir temperature was used in all the experiments. Recovery factors ranged from 1.7 to 40%. The wide variation was the result of different experimental conditions for pressure and soak time. Both operational parameters were found to significantly affect the recovery. Increasing soak time at constant pressure consistently resulted in significant increase in recovery. The increase varied from 78 to 464% for different pressures and oil composition. Similarly, increasing operating pressure at constant soak time resulted in significant increase in recovery factor from 44 to 338% depending on soak time and oil composition. Unlike the typical response during CO2 EOR in conventional rocks, in organic rich shale, further pressure increases beyond the slim-tube MMP continued to increase the recovery factor significantly. In all runs, almost all oil recovery occurred within three days from the start of the experiment, and in all huff-and-puff tests the highest rate of recovery was observed in the first cycle, implying oil recovery with CO2 is a fast process, in comparison to oil re-saturation of the samples which occurs at a significantly slower rate. This investigation demonstrates CO2 EOR is a technically feasible method to extract significant amounts of crude oil from organic rich shale reservoirs and it provides operational understanding of how to manage pressure and soak time to maximize recovery. The recovery factors obtained in this investigation, in the context of the vast reserves of crude oil contained in organic rich shale, can sustain a second shale revolution and further capitalize oilfield infrastructure.
- North America > United States > Texas (1.00)
- North America > Canada > Alberta (0.68)
- North America > United States > Montana (0.68)
- North America > United States > North Dakota > Mountrail County (0.46)
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.93)
Abstract There is considerable and timely interest in oil and condensate production from liquid-rich regions, placing emphasis on the ability to predict the behavior of gas condensate bank developments and saturation dynamics in shale gas reservoirs. As the pressure in the near-wellbore region drops below the dew-point, liquid droplets are formed and tend to be trapped in small pores. It has been suggested that the injection of CO2 into shale gas reservoirs can be a feasible option to enhance recovery of natural gas and valuable condensate oil, while at the same time sequestering CO2 underground. This work develops simulation capabilities to understand and predict complex transport processes and phase behavior in these reservoirs for efficient and environmentally friendly production management. Although liquid-rich shale plays are economically producible, existing simulation techniques fail to include many of the production phenomena associated with the fluid system that consists of multiple gas species or phases. In this work, we develop a multicomponent compositional simulator for the modeling of gas-condensate shale reservoirs with complex fracture systems. Related storage and transport mechanisms such as multicomponent apparent permeability (MAP), sorption and molecular diffusion are considered. In order to accurately capture the complicated phase behavior of the multiphase fluids, an equation of State (EOS) based phase package is incorporated into the simulator. Due to the large capillary pressure that exists in the nanopores of ultra-tight shale matrix, the phase package considers the effect of capillary pressure on phase equilibrium calculations. A modified negative-flash algorithm that combines Newton's method and successive substitution iteration (SSI) is used for phase stability analysis under the effect of capillary pressure between oil and gas phases. In addition, a lower-dimensional discrete fracture and matrix (DFM) model is implemented. The DFM model is based on unstructured gridding, and can accurately and efficiently handle the non-ideal geometries of hydraulic fracture in stimulated unconventional formation. Optimized local grid refinement (LGR) is employed to capture the extremely sharp potential gradient and saturation dynamics in the ultra-tight matrix around fracture. We apply the developed simulator to study the combined effects of capillary pressure and multicomponent storage and transport mechanisms that are closely associated with the phase behavior and hydrocarbon recovery in gas-condensate shale reservoirs. We present preliminary simulation studies to show the applicability of CO2 huff-n-puff for the purpose of enhanced hydrocarbons recovery. Several design components such as the number of cycles and the length of injection period in the huff-n-puff process are also briefly investigated.
- Europe (1.00)
- North America > United States > Texas (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (1.00)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- (13 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Gas-condensate reservoirs (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)