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Mud gas data from drilling operations provide the very first indication of the presence of hydrocarbons in the reservoir. It has been a dream for decades in the oil industry to predict reservoir gas and oil properties from mud gas data, because it would provide knowledge of the reservoir fluid properties in an early stage, continuously for all reservoir zones, and at low costs. Previous efforts reported in the literature did not lead to a reliable method for quantitative prediction of the reservoir fluid properties from mud gas data. In this paper, we propose a novel approach based on machine learning which enables us to predict gas oil ratio (GOR) from advanced mud gas (AMG) data.

The current work is based on a previous successful pilot in unconventional (shale) reservoirs. Our aim is to extend the results of the pilot study to conventional reservoirs. In general, prediction of reservoir fluid properties is more challenging for conventional reservoirs than for unconventional reservoirs, due to the complexity of petroleum systems in conventional reservoirs. Instead of building a model directly from AMG data, we trained a machine learning model using a well-established reservoir fluid database with more than 2000 PVT samples. After thorough investigation of compositional similarity between PVT samples and AMG data, we applied the model developed from PVT samples to AMG data.

The predicted GORs from AMG data were compared with GOR measurements from corresponding PVT samples to assess the accuracy of the GOR predictions. The results from 22 wells with both AMG data and corresponding PVT samples show large agreement between prediction vs. measurement. The accuracy of the predictive model is much higher than previous results reported in the literature. In addition, a Quality Check (QC) metric was developed to efficiently flag low-quality AMG data. The QC metric is vital to give confidence level for GOR prediction based on AMG data when PVT samples are not available.

The study confirms that AMG data can be used as a new data source to quantitatively predict continuous reservoir fluid properties in the drilling phase. The method can be used to optimize wireline operations and for some cases, it provides a unique opportunity to acquire reservoir fluid data when conventional fluid sampling or use of wireline tools is not possible. After high-quality PVT data becomes available in the wireline logging phase, the continuous GOR prediction can be further improved and used to determine reservoir fluid gradient and reservoir compartmentalization.

As an enhanced oil recovery method (EOR), chemical flooding has been implemented intensively for some years. Low Salinity WaterFlooding (LSWF) is a method that has become increasingly attractive. The prediction of reservoir behaviour can be made through numerical simulations and greatly helps with field management decisions. Simulations can be costly to run however and also incur numerical errors. Historically, analytical solutions were developed for the flow equations for waterflooding conditions, particularly for non-communicating strata. These have not yet been extended to chemical flooding which we do here, particularly for LSWF. Dispersion effects within layers also affect these solutions and we include these in this work.

Using fractional flow theory, we derive a mathematical solution to the flow equations for a set of layers to predict fluid flow and solute transport. Analytical solutions tell us the location of the lead (formation) waterfront in each layer. Previously, we developed a correction to this to include the effects of numerical and physical dispersion, based on one dimensional models. We used a similar correction to predict the location of the second waterfront in each layer which is induced by the chemical's effect on mobility. In this work we show that in multiple non-communicating layers, material balance can be used to deduce the inter-layer relationships of the various fronts that form. This is based on similar analysis developed for waterflooding although the calculations are more complex because of the development of multiple fronts.

The result is a predictive tool that we compare to numerical simulations and the precision is very good. Layers with contrasting petrophysical properties and wettability are considered. We also investigate the relationship between the fractional flow, effective salinity range, salinity dispersion and salinity retardation.

This work allows us to predict fluids and solute behaviour in reservoirs with non-communicating strata without running a simulator. The recovery factor and vertical sweeping efficiency are also very predictable. This helps us to upscale LSWF by deriving pseudo relative permeability based on our extension of fractional flow and solute transport into such 2D systems.

Application of polymer flooding as a chemical Enhanced Oil Recovery (EOR) has increased over recent years. The main type of polymer used is partially hydrolyzed polyacrylamide (HPAM). This polymer still has some challenges especially with shear stability and injectivity that restrict its utility, particularly for low permeability reservoirs. Injectivity limits the possible gain by acceleration in oil production due to polymer flooding. Hence, good polymer injectivity is a requirement for the success of the operation. This paper aims to investigate the influence of formation permeability on polymer flow in porous media.

In this study, a combination of core flooding with rheological studies is presented to evaluate the influence of permeability on polymer in-situ rheology behavior. The in-situ flow of HPAM polymers has also been studied for different molecular weights. The effect of polymer preconditioning prior to injection was studied through exposing polymer solutions to different extent of mechanical degradation.

Results from this study reveal that the expected shear thinning behavior of HPAM that is observed in rheometer measurements is not observed in in-situ rheology in porous media. Instead, HPAM in porous media exhibits near-Newtonian behavior at low flow rates representative of velocities deep in the reservoir, while exhibiting shear thickening behavior at high flow rates representative of velocities near wellbore region. The pressure build-up associated with shear thickening behavior during polymer injection is significantly higher than pressure differential during water injection. The extent of shear thickening is high during the injection of high Mw polymer regardless of cores' permeability. In low permeable Berea cores, shear thickening and mechanical degradation occur at lower velocities although the degree of shear thickening is lower in Berea to that observed in high permeable Bentheimer cores. This is ascribed to high polymer retention in Berea cores that results in high residual resistance factor (RRF). Results show that preshearing polymer before injection into porous media optimizes its injectability and transportability through porous media. The effect of preshearing becomes favorable for the injection of high Mw polymers into low permeability formation.

This study discusses polymer in-situ rheology and injectivity, which is a key issue in the design of polymer flood projects. The results provide beneficial information on optimizing polymer injectivity, in particular, for low permeability porous media.

Utilisation of microorganisms as an enhanced oil recovery (EOR) method has attracted much attention in recent years because it is a low-cost and environmentally friendly technology. However, the pore-scale mechanisms involved in MEOR that contribute to an additional oil recovery are not fully understood so far. This work aims to investigate the MEOR mechanisms using microfluidic technology, among others bioplugging and changes in fluid mobilities. Further, the contribution of these mechanisms to additional oil recovery was quantified.

A novel experimental setup that enables investigation of MEOR in micromodels under elevated pressure, reservoir temperature and anaerobic and sterile conditions was developed. Initially, single-phase experiments were performed with fluids from a German high-salinity oil field selected for a potential MEOR application: Brine containing bacteria and nutrients was injected into the micromodel. During ten days of static incubation, bacterial cells and in-situ gas production were visualised and quantified by using an image processing algorithm. After that, injection of tracer particles and particle image velocimetry were performed to evaluate flow diversion in the micromodel due to bioplugging. Differential and absolute pressures were measured throughout the experiments. Further, two-phase flooding experiments were performed in oil wet and water wet micromodels to investigate the effect of in-situ microbial growth on oil recovery.

In-situ bacteria growth was observed in the micromodel for both single and two-phase flooding experiments. During the injection, cells were partly transported through the micromodel but also remained attached to the model surface. The increase in differential pressure confirmed these microscopic observations of bioplugging. Also, the resulting permeability reduction factor correlated with calculations based on the Kozeny-Carman approach using the total number of bacteria attached. The flow diversion of the tracer particles and the differences in velocity field also confirmed that bioplugging occurred in the micromodel may lead to an improved conformance control. Oil viscosity reduction due to gas dissolution as well as changes in the wettability were also identified to contribute on the incremental oil. Two-phase flow experiments in a newly designed heterogeneous micromodel showed a significant effect of bioplugging and improved the macroscopic conformance of oil displacement process.

This work gives new insights into the pore-scale mechanisms of MEOR processes in porous media. The new experimental microfluidic setup enables the investigation of these mechanisms under defined reservoir conditions, i.e., elevated pressure, reservoir temperature and anaerobic conditions.

Chemical Enhanced Oil Recovery leads to substantial incremental costs over waterflooding of oil reservoirs. Reservoirs containing oil with a high Total Acid Number (TAN) could be produced by injection of alkali. Alkali might lead to generation of soaps and emulsify the oil. However, the generated emulsions are not always stable.

Phase experiments are used to determine the initial amount of emulsions generated and their stability if measured over time. Based on the phase experiments, the minimum concentration of alkali can be determined and the concentration of alkali above which no significant increase in formation of initial emulsions is observed.

Micro-model experiments are performed to investigate the effects on pore scale. For injection of alkali into high TAN number oils, mobilization of residual oil after waterflooding is seen. The oil mobilization is due to breaking-up of oil ganglia or movement of elongated ganglia through the porous medium. As the oil is depleting in surface active components, residual oil saturation is left behind either as isolated ganglia or in down-gradient of grains.

Simultaneous injection of alkali and polymers leads to higher incremental oil production in the micro-models owing to larger pressure drops over the oil ganglia and more effective mobilization accordingly.

Core flood tests confirm the micro-model experiments and additional data are derived from these tests. Alkali co-solvent polymer injection leads to the highest incremental oil recovery of the chemical agents which is difficult to differentiate in micro-model experiments. The polymer adsorption is substantially reduced if alkali is injected with polymers compared with polymer injection only. The reason is the effect of the pH on the polymers. As in the micro-models, the incremental oil recovery is also higher for alkali polymer injection than with alkali injection only.

To evaluate the incremental operating costs of the chemical agents, Equivalent Utility Factors (EqUF) are calculated. The EqUF takes the costs of the various chemicals into account. The lowest EqUF and hence lowest chemical incremental OPEX are incurred by injection of Na₂CO₃, however, the highest incremental recovery factor is seen with alkali co-solvent polymer injection. It should be noted that the incremental oil recovery owing to macroscopic sweep efficiency improvement by polymer needs to be taken into account to assess the efficiency of the chemical agents.

The YKL condensate gas reservoir is one of the biggest condensate gas reservoirs in China and has been developed more than 10years. At present, the combination of subdivision layer, production speed optimization and horizontal well drilling has been the key to economically unlocking the vast reserves of the YKL condensate gas. The primary recovery factor, however, remains rather low due to high capillary trapping and water invasion. While primary depletion could result in low gas recovery, CO₂ flooding provides a promising option for increasing the recovery factor.

The objective of this work is to verify and evaluate the effect supercritical CO₂ on enhancing gas recovery and analyze the feasibility of CO₂ enhance gas recovery (CO₂ EGR) of condensate gas reservoir.

Firstly, novel phase behavior experimental procedures and phase equilibrium evaluation methodology for gas-condensate phase system mixed with supercritical CO₂ with high temperature were presented. A unique phase behavior phenomena was also reported. Then, CO₂ floodingmechanism in condensate gas reservoir was analyzed and clarified based on experiments. Finally, a series of numerical simulation work were conducted as an effective and economical means to maximize natural gas recovery with the lowest CO₂ breakthrough by varying strategies, including CO₂ injection rate, injection composition, andinjection timing. Meanwhile the CO₂ storage volumes of different strategies were calculated.

The results show that higher gas recovery factor can be achieved with CO₂ injection through appearing interphase between two fluids, maintaining reservoir pressure, driving gas like "cushion" and controlling water invasion. All strategies have moderate to significant effects on gas production. The control of injection and production ratio needs to be balanced between pressure transient and CO₂ breakthrough over the producer to obtain the maximum gas production. The varying injection pressure shows a positive effect of enhancing gas production. Numerical simulation indicated that the recovery of gas reservoir was improved by around 10 percent. The total CO₂ storage would be around 30-40% HCPV.

The research showed that CO₂ flooding presents a technically promising method for recovering the vast condensate gas while extensively reducing greenhouse gas emissions.

Recent studies have indicated that Huff-n-Puff (HNP) gas injection has the potential to recover an additional 30-70% oil from multi-fractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution), and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example.

Compositional simulations are conducted which incorporate a tuned PVT model and a set of measured cyclic injection/compaction pressure-sensitive permeability data. Markov chain Monte Carlo (McMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The McMC process is accelerated by employing an accurate proxy model (kriging) which is updated using a highly adaptive sampling algorithm. Gaussian Processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ-σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions.

The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half-length, are narrower, while wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of around 1.5 months, a production time of around 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubble point are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production.

The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced machine learning techniques for optimization under uncertainty, resulting in better decision making.

Two upscaling exercises performed in 2013-14 and 2017-18 on two onshore green fields with conventional to viscous oil are presented, for which the upscaling tried to compensate the effects of grid coarsening, in particular the increase of numerical dispersion and the decrease of heterogeneity. Our methodology was to adjust the water/oil relative permeabilities called pseudo KRs in the coarse scale simulation, in order to reproduce the behavior in terms of pressure, rates, saturations and concentrations of the fine scale model, which was using microscopic rock KRs based on laboratory data.

As the upscaling depends on the fluid injected, it was done separately for waterflood and polymer flood. When done with polymer flood, the concentration of polymer had to be history matched also mainly by adjusting the Todd-Longstaff mixing parameter in addition to the KRs. As upscaling is case dependent, it was performed on several geological models, varying heterogeneity and grid size, but also rock KRs and even precocity of the polymer flood after some waterflood, to test the robustness of the approach.

It was found that pseudo-KRs for waterflood could be slightly degraded for viscous oils, whereas the upscaling was more neutral for conventional oils. This correlates well with field observation for viscous oils, where water production occurs generally a bit quicker than what numerical simulation predicts when using rock KRs, in absence of upscaling.

For polymer floods, which were considered in secondary or early tertiary mode, pseudo KRs were generally improved, mainly because the polymer steepened the saturation fronts, which can be well represented only with small lateral grid size.

The result of both upscaling exercises was that the increment of polymer flood versus waterflood was noticeably higher when computed on high resolution modelling. This is equivalent to saying that when using pseudo KRs resulting from this high resolution matching, the polymer increment on coarse grid is significantly higher than if computed without pseudo KRs. This improves the economic evaluation of the project, increasing the willingness to de-risk and implement early polymer floods on these fields.

Curved pipes are essential components of subsea process equipment and some part of production pipeline and riser. So far, most of the studies on of wax deposition and the possible mitigation strategies have been carried out using straight pipelines, with little attention given to curved pipes. Therefore, the objective of this study is to use an experimental flow loop designed and assembled in the lab to study and understand the mechanisms and variable parameters that affect wax depositional behaviour under the single-phase flow. Series of experiments were carried out with pipes curvatures of 0, 45 and 90-degree at different flow rates (2 and 11 L/min). The sequence in which the bends are incorporated creates non-uniformity of boundary shear, flow separation, and caused isolation of fluid around the bends that affect wax deposition, which depends on flow regimes – Reynolds number along with the radius of curvature of the bend. Prior to the flow loop experiment, the waxy crude oil was characterized by measuring the viscosity, WAT (30°C), pour point (25.5°C), n-Paraffin distribution (C10 - C67), and the saturated/aromatic/resin/asphalte (SARA) fractions

Results of this study shows that the wax deposit thickness decreases at higher flow rate within the laminar (Re<2300) and turbulent (Re>2300) flow regimes. It was observed that the deposition rate was significantly higher in curved pipes, about 8 and 10% for 45 and 90-degree, respectively in comparison to the straight pipe for all flow conditions. Increase elevation of the curved pipe, however, led to a more wax deposition trend; where a higher percentage of wax deposit was observed in 45-degree compared to 90-degree curved pipe. This trend was due addition of gravity forces to the frictional forces - influenced by the physical mechanisms of wax deposition mainly molecular diffusion, shear dispersion and gravity settling. From the results of this study, a new correlation between wax deposit thickness and pressure drop was developed. A relationship was established between wax deposit thicknesses, bend angle in pipes and wax deposition mechanisms with a reasonable agreement with published data, especially for steady state condition. Therefore, this study will enhance the understanding of the wax deposition management and improve predictions for further development of a robust mitigation strategy.

The field-scale design of chemical enhanced oil recovery (cEOR) processes requires running complex numerical models that are computationally demanding. This paper provides an efficient screening platform for the cEOR feasibility study by presenting five artificial neural network (ANN) based models. We constructed 1,100 ANN training cases using CMG-STARS to capture the variation in reservoir petrophysical properties and the range of injected chemicals properties for a five-spot pattern. The design parameters were coupled with the reservoir properties using several functional links to optimize the ANN models and improve their performances. The training cases were employed using back-propagation methods to construct one forward model (Model #1) and four inverse models. Model #1 predicts reservoir response (i.e., oil rate, water cut, injector bottomhole pressure, cumulative oil) for known reservoir characteristics (i.e., permeability, thickness, residual oil saturation, chemical adsorption) and project design parameters (i.e., pattern size, chemical slug size and concentration), Model #2 predicts reservoir characteristics by history matching the reservoir response, and Model #3 predicts project design parameters for known reservoir response and characteristics. Models #4 and #5 predict project design parameters for a targeted cumulative oil volume and project duration time, which is useful for economical evaluation before the implementation of cEOR projects.

The validation results show that the developed ANN-based models closely predict the numerical results. In addition, the models are able to reduce the computational time by four orders of magnitude, which is significant considering the complexity of cEOR modeling and the need for reliable and efficient tools in building cEOR feasibility studies. In terms of accuracy, Model #1 has a prediction error of 5% whereas the error for other four inverse ANN models is about 20–40%. To enhance the performance of the inverse ANN models, we changed the ANN structure, increased training cases, and used functional links, which slightly reduced the error. Further, we introduced a back-check loop that uses the predicted parameters from the inverse ANN models as inputs in the forward ANN model. A comparison of back-check results for the reservoir response with the numerical results delivers a relatively small error of 10%, revealing the non-uniqueness of solutions obtained from the inverse ANN models.