Upstream Oil and Gas Technology最新文献

The Permian Basin in Texas is the largest and fastest-growing oil and gas producing region in the United States. Along with this growth, there have been increased methane emissions and natural gas flaring. The volume of flared gas can be measured by various methods. In this study, flared gas volumes obtained from satellites are compared with the flared gas volume and oil production reported by the operators to the State of Texas. The novelty of this study arises from the fine-grained perspective with which it is conducted, that is, the data points are spatially narrowed down to ten different flare sites located in the Permian Basin and temporally narrowed down to monthly volume comparisons. It is found that satellite data matches reported data at some sites, while in other sites it is higher or lower than the operator-reported data. The trend in flaring is compared with oil production from the wells associated with the ten flare sites. At the sites where the data matched the gas flaring was also correlated with the oil production data. This suggests that the reported flare volumes are more accurate (i.e. match with the satellite) under routine flaring, and are less accurate for episodic flaring. Only five out of the ten flares showed a correlation with oil production. For the ten sites, cumulatively, the satellite observed volumes were 16% of the operator-reported volume in 2018, 21% in 2019, 41.5% in 2020, and 111% in 2021. A discussion is included on how changes in regulations may affect these comparisons going forward.

Batch transfer results in contamination over the length of travel of the fluids in product pipelines. Mathematical models have been in use. Machine learning with Python, due to higher efficiency was applied to determine contamination length. Six models were developed and the best with an accuracy of 97.4% and RMSE score of 262.5 was developed. It predicts with higher precision and also accurately ranks the input variables in order of their influence on transmix length. The distance of travel had the highest influence on the amount of contamination in a pipeline, followed by Reynolds number and pipe diameter.

Dilution onsets are an attractive and convenient way to study the tendency of asphaltenes to precipitate from a given crude, since they do not require high-pressure equipment and dead oil samples utilized are quite easy to get in comparison to bottom hole samples. In addition, the so-called indirect method developed and proposed in the last decade presents some advantages over previous direct methods, mainly that precipitation of smaller particles can be detected without complications.

This technique was implemented in this work as a first study of onsets for Argentinian crudes, considering two light and two medium viscous oils, with n-pentane and n-heptane as precipitating agents. Several aspects of the method implementation are discussed, and special attention is paid to the dilution of the supernatant after centrifugation. It was observed that dilution of the supernatant in toluene should be adjusted according to oil density. Onsets were detected not only for the two heavier fluids with important asphaltene contents, but also for one of the light oils studied. In agreement with previous publications, it is seen that a shorter and more volatile alkane, in this case n-pentane, anticipates the onset in comparison to n-heptane.

The use of the measured onset for tuning a compositional model for the reservoir fluid is illustrated for a light oil produced from the Vaca Muerta Formation. Nevertheless, it is shown that the type of compositional information used as input for characterizing the heavier fractions can have an important impact on the prediction of the asphaltene precipitation behavior at high pressures.

The discharge coefficient (C_d) affects the perforation pressure drop during the hydraulic fracturing treatments. Given the changes in completion procedures, especially with horizontal wells, this absence of research on the C_d value indicates that many times over-simplification of the term is applied in practice. The research outlined in this paper focuses solely on the C_d term and the various conditions that are encountered downhole and the resulting impacts on perforation friction.

This research provides an understanding into the influence of various factors on the discharge coefficient. Perforation hole size, perforation plate thickness, perforation hole geometry, downstream restrictions, friction reducer, fluid viscosity and horizontal behaviors are investigated in this work. In this study, a low pressure setup was built to investigate the effect of the previous factors on the discharge coefficient and subsequently the pressure loss through the perforations in hydraulic fracturing treatments.

The experimental results show that the perforation hole size does not impact the discharge coefficient. However, perforation plate thickness, friction reducer fluid loading and fluid viscosity has a positive relation with the discharge coefficient value. In other words, a clear increase in the C_d value was observed when these parameters were increased for all the conducted tests. For different perforation hole geometries, the results show that the smooth and tapered edge hole has a larger C_d value than the same shaped sharp-edge hole. When a downstream restriction is installed, the C_d value increases as the distance between the downstream reflection plate and the perforated plate increases. The results of the horizontal well behaviors show that the C_d value of the perforated horizontal casing is slightly higher than C_d value for the perforated plate. Overall, this study on the effect of influencing factors on the perforation C_d value can help form a better understanding of perforation pressure drop behaviors and modify the design of limited-entry techniques.

Hydraulic Fracture Stimulations (HFS) are designed to improve well production while minimalizing environmental and geomechanical stability issues such as unintended “frack hits”, excessive height growth, and unintended break-through to thief zones or water production zones. Planning a fracture geometry with the optimal height, aperture, and length is the goal, and factors affecting the geometry include geological and geomechanical properties (natural fractures, bedding fabric, stresses, geomechanical properties, permeability, etc.) play significant roles. These properties are usually predetermined and are considered as design inputs; other parameters such as the pumping rate, fluid viscosity and density, and proppant concentration and schedule are determined (designed) when proposing a stimulation. The proper HFS design should take into account all technical and environmental aspects, meaning that the design (operational) parameters are chosen based on geological factors and the designer's experience to target a desired fracture geometry. HFS design is therefore the interaction of Geometry and Geology, the G&G interaction. A commercial two-dimensional coupled discrete element software, UDEC^TM, is used to study geometry outcomes from ranges of geology inputs and designed operational parameters. The sensitivity analysis methodology employs the Morris technique to assess which geological and operational parameters have greater impacts the geometry of a single vertical fracture. Emphasizing parameter ranges more applicable to shallow formations ensures that the results can help assess fracture height outcomes near the surface and groundwater, where variability in fracture height is of environmental concern.

Geomechanics; Hydraulic Fracture Stimulation; Sensitivity Analysis; Fracture geometry; Fracture Height

Optimum well controls to maximize net present value (NPV) in a waterflooding operation are often obtained from an iterative process of employing numerical reservoir simulation and optimization algorithms. It is often challenging to implement gradient-based optimization algorithms because of the large number of variables and the complexities to embed the optimization algorithm in the simulator solving workflow. Approaches based on repeated model evaluation are easier to implement but are often time-consuming and computationally expensive.

This work proposes the use of Artificial Neural Networks (ANN) to replicate the numerical reservoir simulation outputs. The ANN model is used to estimate cumulative oil production, cumulative water injection, and cumulative water production based on sets of well control values, i.e. flowing bottom-hole pressure. Then, the ANN model is combined with the genetic algorithm (GA) optimization (a derivative-free optimization) to find the optimum well controls that maximize the NPV of a synthetic reservoir model. The optimization results of this ANN-GA model were compared against the results of using the traditional approach of applying the genetic algorithm directly on the numerical reservoir model.

The ANN model successfully reproduces the results of the numerical reservoir model with a low average error of 1.89%. The ANN-GA model successfully finds optimal operational conditions that are identical to those found by using GA and the original reservoir model. However, the running time was lowered by 96% (43 h faster) when compared to the optimization scheme using the original reservoir model. The optimal solution increases the NPV by 22.2% when compared to the base case.

Unstable foams quickly lose their valuable properties. This article presents the results of an experimental study conducted on the drainage of aqueous foams at elevated pressures. During the investigation, the foam was generated and circulated in a flow loop. First, its rheology was measured to ensure proper foam generation. Then, its drainage was determined by trapping it in a vertical test section and measuring the pressure profile. The results show that increasing pressure reduces foam drainage, indicating foam stabilization at high pressures. In addition, column height decreases foam drainage because the drainage rate varies along the axis of the column.

Electrical submersible pumps (ESP) are one of the most utilized artificial lift methods to increase oil recovery. Conventional ESPs operate using an asynchronous three phase induction motor. Although this type of motor has proven to be effective in powering the ESP, it has several disadvantages and is prone to failure especially in severe downhole conditions. This research investigates the replacement of the conventional asynchronous three phase induction motor with a synchronous two-to-four pole permanent magnetic motor (PMM) for a prolonged ESP mean time between failure and a higher output with lower energy requirements. PMMs have been applied in multiple fields worldwide when using ESP with different string designs and operating conditions. The advantages of using PMMs with ESP have varied significantly depending on the wellbore properties including depth, temperature, and pressure; reservoir rock properties including unconsolidated formations; and reservoir fluid properties including the presence of wax, asphaltene, carbon dioxide, hydrogen sulfide, and high total dissolved salts. Since the performance of the PMM with ESP varies from one reservoir and formation to another, it is important to set a guideline to the application of ESP-PMM based on the aforementioned properties. This research therefore provides proper screening criteria for the application of PMM with ESP based on wellbore, formation rock, and reservoir fluid properties. The screening criteria are constructed based on a comprehensive database of real field implementations of PMM with ESP in more than 10 different countries worldwide.

Abstract

Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gained from other deep neural network models trained on adjacent counties into the county of interest. The paper uses data from more than 6000 shale gas wells across 17 counties from Texas Barnett and Pennsylvania Marcellus shale formations to test the capabilities of transfer learning. The results reduce the forecasting error between 11% and 47% compared to the widely used Arps decline curve model.

The unique properties of viscoelastic surfactants (VES) make them one of the essential chemicals used in oil fields. However, the temperature limitations of some VES, as well as their relatively high cost, pose significant challenges for research and chemical organizations to develop new generations of VES that effectively overcome these restrictions. This paper vastly evaluated the spent acid preparation methods and their effects on the rheological profile of VES. The data presented expressed the process of screening and examining the rheological behaviors of new VES under different temperatures and shear rates at optimum controlled pH and pressure.