Upstream Oil and Gas Technology最新文献

In the global economy, crude oil is the most strategic commodity, which plays an important role in determining many regional and international equations. The oil market is the most complex, turbulent, and opaque international financial market. The conditions of these financial markets are well matched to the gray analysis environment. Therefore, the researchers of the present study have proposed the gray prediction model. The results show that using the gray prediction model causes the performance to be significantly improved. The main aim of this paper is to find a model to provide clear projections for the future of oil prices.

Decline curve analysis (DCA)—the extrapolation of a production curve model fitted to a well’s past production—remains the standard approach for forecasting unconventional oil and gas production. A scaling curve based on a fractured shale gas reservoir model was recently proposed as a way of connecting this approach with underlying physics but as this paper shows, it actually generates worse predictions than the traditional non-physical modified Arps curve. DCA is fundamentally an ill-posed inverse problem with the defining characteristic of model sloppiness, or parameter correlation. Today’s unconventional resource forecasts can be substantially improved by using information from offset wells to reduce ill-posedness through Tikhonov regularization. This versatile approach nearly matches a deep neural network approach introduced here, which has practical limitations but offers a model-neutral benchmark of achievable extrapolation accuracy. There is a natural connection between regularization and a Bayesian formulation which is also highlighted. This paper evaluates long-term forecasting accuracy for these techniques using historic production data from 4457 Barnett shale wells, and reveals that the overlooked step of regularization is more critical than choice of model.

Lost circulation is a unique challenge unlike other factors contributing to non-productive time (NPT). Due to the variability in the nature and type of lost circulation prone formations, there is no universal solution to this challenge. This publication presents a new approach to guide the decision-making process of which and when to apply a certain treatment as compared to another. If implemented correctly, a significant reduction in NPT related to lost circulation can be expected. In addition, the examination of the cost of each treatment and the NPT was conducted. Lost circulation events for three carbonate formations which are the Dammam (dolomite), Hartha (limestone), and Shuaiba (limestone) were gathered from over 1000 wells. The treatments were categorized based on the type of loss, cost, and type of formations. This work uses decision tree analysis (DTA) and expected monetary value (EMV) in the decision-making process. Thousands of treatment scenarios were considered to treat partial, severe, and complete losses. Two criteria were utilized to choose the treatment strategies for each type of loss. The first criterion is that the treatment strategy has to have the lowest EMV, and the second criterion is the treatment strategy has to be practically applicable in the field. Both criteria have to be met in order to choose the treatment strategy. All treatment strategies end up with liner hanger if the lost circulation did not stop after applying all treatments. Moreover, this study provides comprehensive treatment strategies to handle lost circulation in three carbonate formations to assist the drilling personnel to deal with lost circulation efficiently and cost-effectively. This study provides a new method to select the best lost circulation treatment strategy for each type of loss and three carbonate formations. Due to the inconsistency of methods to respond to the lost circulation problem, this study can serve a reference to handle lost circulation in any formation worldwide.

The main objective of this study is to investigate the effect of Graphene oxide (GO) nanostructures in preventing the formation of waxy sediments, and to assess its impact on some crude oil physical properties. By functionalization of graphene oxide with polyethylene glycol (PEG), the GO-PEG nanostructure was synthesized. The nanostructure was then examined using X-ray diffraction analysis, scanning electron microscope, and infrared spectroscopy. It became clear that polymer was connected to graphene and the graphene platelet structure was maintained after modification with the polymer. Then, GO-PEG nanostructure was added to the crude oil in different concentrations of 100, 200, 400, 800, and 1000 ppm and the oil pour point, appearance temperature of the wax, viscosity, and the rate of oil deposition were examined for each sample. The results revealed that the oil pour point was significantly reduced by adding nanostructure. This temperature was decreased from 17 °C in pure crude oil to -5 °C in GO-PEG concentration of 800 ppm. Moreover, the results of calorimetry indicated that the wax appearance temperature was decreased by increasing the nanostructure concentration, and it was reached to 32.2 °C in the final concentration of 800 ppm from 42.1 °C in crude oil as blind sample. The study of sediment thickness in the oil flow via a loop setup indicated that increasing the nanostructure concentration, decreases the amount of sediment. Finally, based on both viscosity and sediment thickness evaluations, the satisfactory concentration of the anti-wax nano-agent was 400 ppm. Consequently, according to the results, this nanostructure could be used as a preventive for sediment formation instead of conventional and costly methods such as heating of crude oil.

To improve the performance, sensibility, rheological properties, fluid loss of the bentonite water-based drilling mud (WBM), and minimizing the fluid loss of the WBM. The impact of bentonite modified with nanoclay (NC) on the sensibility, rheological properties, and fluid loss of the WBM was investigated. Depend on the information collected from different research studies, the percentage of bentonite in the WBM was ranged from 2%-8% as a percentage of the weight of water. The percentage of NC was ranged between 0 and 1% by mass of drilling mud. The tests were performed under various temperature conditions ranged between 25 °C and 85 °C. From the lab studies and numerical examinations, the electrical resistivity (ER) was identified as a sensing property of the WBM so that the rheological properties can be estimated during the construction. The additional 1% NC reduced the ER of the WBM by 15% to 36% depending on the composition of the drilling fluid and the temperature of testing. The nanoclay modification increased the yield stress (yield point) and shear stress limit tolerance of the WBMs measured using Vipulanandan rheological model by 32% to 60%, and the outcome of the model prediction was related with Vocadlo model. The laboratory HPHT fluid loss tests were conducted on the drilling fluids based bentonite modified with NC using an American Petroleum Institute (API) standard test up to 420 min until the end of the fluid loss. Zero fluid loss was obtained when 8% of bentonite drilling mud modified with 1% of NC at 25 °C. The Vipulanandan fluid loss model predicted the short and long-term fluid loss and the upper limit of fluid losses very well.

In fractured reservoirs, the fracture size distribution is wide with strong heterogeneity, and wide fractures are not easily plugged. Conventional plugging methods have limitations in reservoir leakage problems. The matching degree of plugging materials and reservoir fractures is relatively low once small particles are transported deep into the reservoir, which can finally cause reservoir damage. Gel plays an important role in the temporary plugging of fractured reservoirs. However, there are currently few mature gel systems that can maintain good rheological properties at room temperature, good gelation at a high temperature and a sustained plugging strength. In this paper, a high-temperature and high-strength (HTHS) gel system was developed, and its properties were evaluated in the laboratory. The gel is cross-linked by covalent bonds, and its temperature resistance can reach 150 °C. Rheological and gelation tests show that the gel solution exhibits good rheological properties at room temperature and can cross-link into a gel at high temperatures. The gelation time could be flexibly adjusted from 4 to 10 h. The gel has good expansibility and can entirely fill fractures. The plugging test reveals that the plugging pressure can reach 0.25 MPa/cm in a 5-mm fracture, and the strength stability can be maintained for one month. Gelation stability can be maintained in the presence of formation water and drilling fluid. The gel has a good self-breaking capability after a period of time and does not affect reservoir production. This study can provide a plugging solution for fractured reservoirs at high temperatures and pressures.

Inability to accurately model fundamental governing flow equation in a hole has resulted in erotic evaluation of flowing and shut-in bottom hole pressures (BHPs) for aerated fluid drilling in borehole. It is of practical important to derive an exact model for this case without ignoring any pressure resisting terms in the governing thermodynamic equation so as to enhance well control efficiently. An improved hydraulics model has been derived to demonstrate the impact of neglected pressure restriction due to kinetic energy and fluid accumulation in the fundamental energy equation used for predicting flowing and shut-in bottom-hole pressures for aerated mud drilling in petroleum well. These neglected terms have conceived to be a vital reason for the eroneous result between computed value from the existing models and actual value generated from field. The developed model has been tested using the same dataset obtained from the field of investigation by Guo et al and more desirable outcomes were got from the new model than the previous investigators with error margin of 2.7%. Realistic results that evident all pressure transverse behaviors after shut-in for aerated mud drilling in well which include the initial constant pressure regime, unsteady regime, semi-steady regime and stabilized state condition hence pressure transverse at any period of drilling operation has been established. The improved model has demonstrated that inaccuracy in the results of existing models were not only caused by the effect of pressure restriction due to friction as opined by Guo et al but may have due to oversight of all pressure restriction terms in the fundamental equation that govern flow of aerated drilling fluid in petroleum well. The new concept is useful for drilling engineers to estimate flowing and shut in bottom-hole pressure for better control of well stability at all flow conditions during aerated mud underbalanced drilling.

This research investigates the wellbore shear bond strength of nano-TiO₂-containing oil-well cement pastes with particle sizes of 20 and 40 nm. The nanoparticles were picked by the weight of cement at proportions corresponding to 1, 2, 3 and 4%. Test results indicated that nano-TiO₂ significantly enhanced the interfacial shear bond strength. The results also revealed that the strength enhancement was reliant on the nano-TiO₂ particle sizes. The specimens containing nano-TiO₂ 40 nm provided greater strength compared to 20 nm, due to effective pozzolanic activity. By fluctuating the nano-TiO₂ dosages, the optimal replacement content was 3% for all particle sizes. The particle size did not affect the appropriate dosage of nano-TiO₂, it only impacted the bonding strength of the interfaces. With 3% (40 nm) nano-TiO₂, the 3, 7, 14 and 28 days strength increased by 557.38, 504.17, 528.57 and 412.04% respectively. The scanning electron microscope, X-ray diffraction and thermogravimetric technique were used to examine the influence of nano-TiO₂ on the cement-formation bonding.

The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Additionally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.

Tracer technology has gained considerable attention recently as an effective tool in the reservoir monitoring and surveillance toolkit, particularly in improved oil recovery (IOR) operations. Gas flow paths within the reservoir can be quite different from liquid (oil and water) flow paths. This is primarily due to gravity override, and differences in density and relative permeability between the gas and liquid phases.

An Inter Well Gas Tracer Test (IWGTT) is a key monitoring and surveillance tool for any IOR projects. IWGTT should be designed and implemented to track the flow behavior of gas phase. The test generally entails injecting a small amount of unique perflouro-hydrocarbon tracers into the gas phase injectant stream. IWGTT have been conducted on a limited number of fields across the globe, and sample results of some will be presented.

The sampling frequency of the tracers from the producers should be designed carefully to collect the necessary data that will provide insights about the connectivity between the injectors and producers well pairs, gas breakthrough times (“time of flight”), and possible interwell fluid saturations. Different fit-for-purpose unique tracers can be deployed in the subject injector(s) stream and their elution can be monitored in the corresponding up-dip producer(s).

In addition to reservoir connectivity and breakthrough times between injector and producer pairs, an IWGTT helps in optimizing water altering gas (WAG) operations and production strategies for gas injection projects, improve sweep efficiency and ultimately enhance oil recovery. It can also be used to identify source of inadvertent gas leakage into shallow aquifers or soil gas, and help in the planning and placement of future wells.

This paper reviews the workflow and necessary logistics for the successful deployment of an interwell gas tracer test. It will provide the best practices for designing, sampling, analyzing and interpreting a gas tracer deployment. The paper also highlights the benefits of gas tracer data and their usefulness in understanding well interconnectivity and dynamic fluid flow in the reservoir. The results can be used to refine the reservoir simulation model and fine-tune its parameters. This effort should lead to better reservoir description and an improved dynamic simulation model. The challenges associated with IWGTT will also be shared.