Unconventional oil wells exhibit rapid decline in oil production rate and low ultimate oil recovery, even though the lateral drilling and completion technology have advanced drastically in the past decade. The petroleum industry has been seeking to develop economic enhanced oil recovery methods to improve the overall recovery factor. The gas huff-n-puff process has been performed and shown the potential of improving the recovery factor from tight oil reservoirs. The objective of the study was to investigate the performance of huff-n-puff EOR in Midland Basin. The studied section of the field contains 2 horizontal producers. The wells produced on primary production for 3 years. The sector was selected as a candidate for performing gas huff-n-puff to enhance the oil recovery factor. Recently, this huff-n-puff EOR project has been performing in the studied volatile oil field in the Permian Basin. In this study, compositional reservoir simulation was used to predict the performance of enhanced oil recovery. A sector model was built for the area selected as the prospective candidate for gas injection. An Embedded Discrete Fracture Model (EDFM) was used for modeling the fractures and stimulated reservoir volume (SRV). A Peng-Robinson equation-of-state model was prepared based on the early produced samples from the wells. A thorough phase behavior analysis was conducted to understand the miscibility of the injected field gas and the in-situ fluid. A Bayesian Assisted History Matching (AHM) algorithm with a neural-network-proxy sampler was applied to quantify uncertainty and find the best model matches for the pair of wells in the Wolfcamp B and C formations of Midland Basin. From 1400 total simulation runs, the AHM algorithm generated 100 solutions that satisfy predefined selection criteria. Even though the primary production were the same for the two wells, the forecasts were dissimilar. It is discussed that the dissimilarity in huff-n-puff performance between two wells is caused by interwell communications. The well interference through fracture hits play an important role in the studied reservoir. The field data show the pressure communication between the two wells. Also, the injected gas was observed in the offset wells about one month after the start of injection. Several long fractures were added to the reservoir model to capture the characteristics of fracture interference. The prospects of EOR were proven decent for the wells of interest. We reported 29% and 82% incremental recovery for the P50 predictions of wells BH and CH, respectively. The results of field operation have been in agreement with the simulation forecasts after two cycles of gas injection and production.
Water invasion, associated with hydraulic fracturing, often causes hydrocarbon-mobility hindrance, known as water blocking. The effect on productivity is largely dependent on saturation profiles inside fractures and formation matrix. Enhancement to hydrocarbon recovery has been reported in some field cases after shutting in wells for long time periods. Here we conduct numerical-simulation studies to investigate the effect of well shut-in on initial productivity and long-term recovery. We replicate post-fracturing conditions with an extensive fracture network that intermeshes with formation matrix. The models are designed using either logarithmically spaced, locally refined grid or embedded discrete fracture model (EDFM). Starting with varying initial fluid distributions, we compare productivity and recovery of two cases: one that does not start production until after 32 days of shut-in, and another that starts immediately without soaking. For the initial conditions that favor shut-in, we carry out case studies in attempt to find the ideal shut-in conditions for maximum recovery improvement. Results confirm improvement in early productivity after shut-in for all the considered initial fluid-distribution cases. The majority of cases exhibit net gain in total oil recovery. We report improvement in recovery of as much as 5%, owing to spontaneous imbibition. Imbibition of the injected water into formation matrix causes fluid redistribution and favored mobility for the non-wetting phase, and hence enhanced hydrocarbon productivity. The simulations take into account spontaneous imbibition and gravity segregation, but do not consider geo-mechanical forces, water adsorption or chemical reactions. When capillary forces are neglected, well productivity and recovery decrease, even when the well is not shut in. Such observations underline imbibition counter-current flow as an important production mechanism that should not be neglected in shale-oil-reservoir simulations.
Gas production from shale-gas reservoirs constitutes the largest portion of total gas production. The US shale reservoirs are tight and inherently heterogeneous with abundant presence of kerogenic material. Modeling fluid flow in shale reservoirs is complex and still an active field of research. The complexity arises from different flow physics such as pressure flow and diffusion. Many of the field performance forecasts constantly underestimate production from these reservoirs because most of the current models ignore important governing physics. This study provides new insights on diffusion in organic matter, in an effort to correct a main source of underestimation of gas production in shale gas models. In an earlier study, we developed for the first time a detailed diffusion model and showed how pore size distribution and specific surface area of the pores in organic matter can significantly influence gas production. An important parameter controlling the rate of gas release is the diffusion coefficient of gas diffusing into organic matter which appears in the flow equations. One of the methods of estimating the diffusion coefficient is based on analysis of gas uptake into shale samples in a closed chamber. The coefficient is extracted by comparing experimental observations to the solution of diffusion equation in the domain of pore/kerogen interface. If the mathematical representation of the organic matter is inaccurate, the diffusion coefficient will be inaccurate as well, regardless of lab-measurements accuracy. The values reported in the literature are based on the slab-shaped mathematical representation of organic matter, assuming a single scale for diffusion characteristic length. In this study, we implement a multi-scale diffusion model to estimate gas diffusion coefficient in organic matter. The previously reported evaluations are on the order of 10−20 m2/s. Reanalysis of the same set of experimental data using our detailed model suggests the interpretation of the coefficient is largely dependent on the diffusion-length scales being considered. We show that diffusion occurs over multiple time scales and the coefficient could be as much as four orders of magnitude higher than reported. The developed diffusion model is a robust and practical mathematical model and can be implemented in reservoir simulators. The findings of this study shed some light on why production forecasts constantly underestimate gas production from shale gas reservoirs.
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