The Cliff Head is one of the most significant discoveries in the offshore Northern Perth Basin. Hence, understanding the structure and geology of the field is essential to further evaluate the offshore region in the basin. Two structural models were developed with the objective to achieve a better understanding of this field. The first model is focused on the Permian and older strata, while the second model is for the overburden. In addition, reservoir properties models (e.g. porosity model and water saturation model) were developed to better understand the reservoir facies and hydrocarbon distribution. Examination of the structural models has shown that there are two main sets of faults within the Cliff Head area, which can be categorized into the following: the deep Permian faults that are truncated against the Late Permian unconformity, and younger Cretaceous faults that were developed during the Early Cretaceous rifting. It has also shown that the oil accumulation within the field is structurally trapped within Permian aged set of horsts and is mainly reservoired within the Irwin River Coal Measures. The secondary target (e.g. the underlying High Cliff Sandstone) is mostly beneath the regional oil-water contact of −1257.8 m TVDss, except in the highest structural point in the field, where Cliff Head-6 was drilled. The Irwin River Coal Measures in the study area contained four high resolution depositional sequences that displayed a finingupward pattern as depicted by the Gamma Ray log response and are interpreted to have mainly deposited in a fluvial depositional system. The High Cliff Sandstone, in contrast, contained two high resolution depositional sequences that displayed a coarsening upward sequences as supported by Gamma Ray log response and were interpreted to have mainly deposited in marginal marine settings. Reservoir properties modeling was also conducted utilizing the 3D models, where a 3D porosity model was calculated and shows that the Irwin River Coal Measures, in general, exhibit higher porosity distribution than the underlying High Cliff Sandstone, even though the later has coarser and more laterally extensive sand sheets. This is probably attributed to diagenetic porosity reduction within the High Cliff Sandstone caused by the formation waters. The calculated 3D water saturation model also confirms the presence of a single regional oil-water contact within the field and hence, reservoir heterogeneities and fault seal capacities did not affect the hydrocarbon distribution within the field. Finally, all the calculated models (e.g. lithofacies model, porosity model, and water saturation model) were integrated to estimate the recoverable hydrocarbons in place, where the Cliff Head is estimated to contain a total of 15.2 million barrels.

After 30 years of waterflooding, distributed temperature sensors (DTS) data on an oilfield has recorded up to 50% reduction in reservoir temperature from its initial value of 120°C. Literature review showed previous efforts are more inclined towards interpretating DTS data to determine layer contribution in a single well rather than studying the impact of temperature change on the field performance and recovery factor. Changes in reservoir temperature will cause a change in reservoir fluids properties and fluids mobilities and subsequent recovery factor will be impacted. In order to determine the impact of reservoir cooling on IWAG (immiscible water-alternate gas) performance, a comprehensive study has been performed combining coreflood experiment with a 3D reservoir modeling. 3D thermal reservoir model has been constructed and calibrated for the purpose of determining the temperature distribution across the field. The model indicates that with higher injection volume of water; reservoir temperature is lowered with temperature distribution highly influenced by the reservoir properties, injected volume, and fluid properties. The coreflood experiments have been performed under isothermal and reduced temperature using a stack of native cores with average properties closely match the reservoir rock that carry most of the oil in place. The results showed an incremental IWAG recovery factor of up to 4% (STOIIP) higher in the reduced temperature experiment compared to IWAG experiment under isothermal conditions. This research paper has demonstrated that ignoring the effect of reservoir cooling in IWAG project can lead to an underestimated IWAG recovery factor and eventually impacting the overall project economics.

The latest developments in well construction rely on advanced drilling and completion technologies to withstand increased wellbore deviation, tortuosity, and complex geometry challenges necessary to access reservoirs, which can create challenges to cement placement between reservoir zones. Hence, well integrity and reservoir management concerns can arise given the possibility for multiple reservoirs to communicate, resulting in undesired crossflow and water production because of poor cement isolation. Poor cement placement behind casings, ineffective zonal isolation, and well integrity issues have been traditionally addressed using workover or drilling rigs to remove production tubing and perform either a remedial cement squeeze or zonal abandonment for subsequent sidetrack. However, this generally entails production deferral, capital-intensive rig rates, the potential risks of pulling and running production tubing, which often translates in formation damage from killing the well, large wellsite footprint requirements, and higher logistic demands for rig support equipment. This paper describes the systematic process used to identify, diagnose, design, and execute a successful inter-reservoir crossflow mitigation operation in a naturally fractured carbonate formation in Abu Dhabi using a cost-effective solution with rigless coiled tubing-deployed technologies and a hydrocarbon-based microfine cement slurry that activates upon contact with water. The results enabled the operator to restore reservoir integrity, improve reservoir management, and enhance water sweep. Post-operation reservoir evaluation validated the successful use of the technology for rigless zonal isolation using coiled tubing (CT) for the first time in the United Arab Emirates, providing significant cost benefits and new best practices to reduce the water cut and restore wellbore integrity.

Performance evaluations of oil and gas assets are crucial for continuously improving operational efficiency in the mainstream petroleum industry. The success of such evaluations is largely driven by the analysis of the data accumulated during the asset's operational cycle. Usually, the amount of data stored in the databases dramatically exceeds the ability to approach the analysis with traditional spreadsheet-based tools or linear modeling. In this study we use data mining with multivariate predictive analytics and monetize on the value of data by transforming the inferred information into knowledge and further into rigorous business decisions. With the expansion of the Digital Oil Field and transformation into the 4th Industrial Revolution, the oil and gas industry is acquiring tremendous amounts of data that come from disparate sources in a variety of origins, time scales, structures and quality. The underlying variable root-cause relationships are highly non-linear and non-intuitive, and simplistic linear regression methods are suboptimal. We approach the challenge by developing a data-driven workflow that integrates components of artificial intelligence, machine learning and pattern recognition to enhance quantitative understanding of complex data. The sanitized aggregated data set combines 470 horizontal wells, covering 15 numerical (e.g., stimulation interval length, production rates) and categorical (e.g., target zone, proppant type) predictors and the total produced BOE, as the response variable. The objective is to predict an optimal set of variables that maximize the production. We utilize an integrated analytics platform that enables a variety of sophisticated statistical operations on large-scale data: a) comprehensive data QA/QC for outliers, consistency and missing entries; b) Exploratory Data Analysis and visualization; c) feature selection, screening and ranking; d) building and training of multiple machine learning (ML) models for multi-variate regression (e.g. generalized linear model, deep learning, decision tree, random forest and gradient boosted machine); and e) response optimization of an identified "best-performing" ML model for highest prediction accuracy. Our study introduces the initiative to establish concepts best practices for predictive and prescriptive analytics in domains of reservoir simulation, description and asset management. Given the unique volume and information richness of operational data, acquired over decades of production history, the anticipated applications of predictive analytics could expand to drilling optimization, smart data aggregation, well stimulation and equipment maintenance.

The complex downhole tools in upstream oil & gas industry are subjected to internal and external pressure due to variety of fluids ranging from drilling mud to reservoir fluids which dictates downhole tool performance in the well bore. Extensive laboratory testing is needed to quantify the downhole tool performance which can be cost and time intensive process. An alternative approach is to conduct numerical simulations to investigate the real operating scenarios and predict the downhole tool performance including high pressure high temperature (HPHT) conditions which are difficult to set up in laboratory environment. The objective of this work is to underscore importance of advanced simulation technology in evaluating down-hole tool performance. To attest usage of simulations two examples are presented, the first one is utilization of computational fluid dynamic (CFD) simulations to analyze the performance of flow regulation inflow control device (ICD) for challenging multi-phase flow operations to predict onset of cavitation and second is to analyze the hold-down slip indentation dynamic operation using advanced explicit finite element simulation methodology. The completions ICD tool aids in maintaining uniform inflow of production fluid in horizontal and deviated wellbores by providing pressure drop utilizing nozzles. Advanced flow modeling is critical to understand the flow behavior through ICD and check for onset of phase change/cavitation of the production fluid. Three dimensional CFD simulations are conducted using pressure based algorithm for numerically solving mixture multi-phase model coupled with Reynolds-Averaged Navier-stokes (RANS) equation and k-ε turbulence model to predict onset of cavitation for given operating conditions. The dynamic hold down slips operation is important to set up a packer and during this step casing gets indented with the carbide buttons. The setting up of slips can cause localized permanent deformation in the casing and estimating the setting force along with hanging load capacity of the slip system is important to avoid permanent damage to the casing. Three dimensional explicit non-linear FEA is conducted to predict the stresses and deformations in the casing. Multi-phase simulations for ICD are conducted with different nozzle configurations and operating conditions. CFD results highlighted velocities, pressure drop through ICD and also indicated onset of cavitation for provided operating conditions. Advanced modeling results helped in configuring the ICD and operating conditions to avoid cavitation. FEA conducted for hold-down slip predicted the load needed to cause ~0.030" button indentation on casing and also provided insights in collapse analysis of the casing. A detailed presentation of the stress field helped in identifying the loading limits for this system and also probable failure locations due to loading. Advanced simulations helped in assessing the downhole tool performance for given operating envelopes and provided critical insights that delivered confidence in utilizing these tools for field operations.

The onshore Bahrain Field is a multi-reservoir oil and gas field in the Kingdom of Bahrain. Hydrocarbons are found in various stratigraphic intervals from Devonian to Upper Cretaceous in mostly carbonate reservoirs. Clastic reservoirs are scarce in the post-Permian section but dominate in the pre-Permian section. The Lower Cretaceous Kharaib Limestone is an oil reservoir. The unit was tested in different exploratory wells in the early life of the Bahrain Field and it was found to be of low permeability but produced oil. The first phase of field development in Kharaib was initiated in the 1970s and after an initial period of dry oil production, the wells produced for a long time with high water cut (more than 90%). In the early 2000s, a horizontal well drilled in the reservoir provided encouraging results. Main development activities started from 2010 onwards, after Tatweer Petroleum – Bahrain Field Development Company W.L.L. ("Tatweer Petroleum") was formed. Thirty horizontal and fifteen deviated producers (a total of forty-five wells) were drilled during the period 2010–2014 and as a result, oil production from the reservoir increased significantly. However, within a short period of time, most of the wells started producing with high water cut (more than 90%) and image logs recorded in many horizontal wells showed presence of fractures. Based on this observation and 3D seismic analysis, Kharaib was characterized as a fractured reservoir and high water production was attributed to it. Later on, many wells had mechanical failure due to casing corrosion developed against the shallower Shuaiba aquifer. All development activities were subsequently stopped for the reservoir due to economic risk. In 2016, a renewed effort was undertaken to analyze all past data including; regional tectonics, core data, 1970s build-up studies, cement integrity, and image log interpretations. This back-to-basics analysis indicated that the primary cause of high water production in the Kharaib wells is lack of zonal isolation with Shuaiba (casing corrosion wells excluded) and not due to the presence of occasional fractures. Development activities restarted in 2017 that focused on addressing the zonal isolation and casing integrity issues. Seven wells have been drilled to date producing with an average water cut of 22%. More development wells are planned in the future thereby giving a new lease on life to the reservoir.

Reverse time migration (RTM) involves zero-lag cross-correlation of forward extrapolated source function wavefields and backward extrapolated receiver wavefields. For a near surface with complex structures and velocity anomalies, forward propagating the source wavelet generates wavefields containing reflections, near-surface multiples, and scattered direct arrivals. The wavefields are recorded as upgoing arrivals contaminated by the same reflections, near-surface multiples, and scattered signals, which can be critical for imaging near-surface structures and scatterers. Here, we develop a new depth migration, duplex reverse time migration (DRTM) technique to improve imaging of complex near-surface structures. DRTM uses the direct arrival as a source to forward propagate and generate source wavefields, and reversely extrapolated recorded data in a zero-lag cross-correlation imaging condition to generate the final section. The interaction between the data components during cross- correlation can use primaries and multiples to image the near-surface structure correctly. Cross-talk artifacts may exist, but they are comparatively weak. DRTM is demonstrated on both synthetic and field data examples showing an enhanced image in areas with complex near-surface structures compared to conventional RTM imaging methods. The new algorithm can significantly enhance shallow imaging without additional computation costs compared with conventional RTM. It can produce an image with higher resolution and signal-to-noise (S/N) ratio by replacing the source wavelet with the recorded direct arrivals, which include near-surface information necessary to boost the image in areas with near-surface complexity. Since the direct arrivals are one of the most energetic events recorded, the resultant image is typically of high S/N. The wave can also illuminate shallow zones better than primaries in marine environments.

Objectives/Scope This paper will describe a methodology which has been developed as an alternative to four-dimensional (4D) Seismic. The main objective is to track heat conformance over time in the thermally developed "A" Field, Sultanate of Oman. The method has several significant advantages over 4D Seismic, including: Negligible cost and manpower requirements; Provision of close to real-time information and no processing time requirements; No Health, Safety or Environmental exposure, or disruption to ongoing operations. The paper will also demonstrate the power of integrating wide-ranging data sources for effective well and reservoir management. Methods, Procedures, Process The increasingly close well spacing at "A" Field has made Seismic Acquisition progressively more challenging. Conversely, it has created an opportunity to utilize dynamic Tubing-Head Temperatures (THTs) for tracking areal thermal conformance over time. For each month in turn an automated workflow:- Grids the monthly THT averages; Integrates the production and injection data, represented as bubble plot overlays; Adds the top reservoir structure from the subsurface model, highlighting structural dip, and fault locations. Morphing (movie) software then interpolates the monthly images to create a smoothly transitioning "Heat Movie". Results, Observations, Conclusions The Heat Movie demonstrates the general effectiveness of the Development in terms of warming the reservoir over time. This in turn is reducing the oil viscosity and increasing production. However, it also highlights temperature anomalies that can be linked to geological features such as faults and high permeability layers. Identification of these anomalies may underpin decisions to optimise the thermal development. In addition to the Movie, time-lapse images can be created for any chosen period. This is similar to 4D Seismic, but more powerful, since the period can be directly linked to significant field milestones, for example equal time periods before and after upgrading the steam generation process. Proof of Concept was demonstrated in early 2018, and the technique has already been deemed sufficiently mature to utilize it for tracking and managing Thermal Conformance in place of 4D Seismic. This is resulting in annual cost savings of millions of dollars and man-years of staff time. Novel/Additive Information One potential advantage of 4D Seismic is highlighting vertical conformance. Although this is not possible using THTs alone, at "A" Field the plan is to mitigate this by integrating data from ongoing Distributed Temperature Sensing (DTS) and well temperature surveys. Regarding applicability, the workflow can be adapted for other objectives, for example creating a movie of surface uplift and/or subsidence integrated with bubble plots of production and injection data, or water breakthrough for wells with downhole gauges, in water flood developments. In addition to describing the methodology underpinning this innovative approach, this paper will also discuss the vision for further improving the workflow and expanding the functionality.

Knowing the source of excessive produced water from oil wells in complex carbonate reservoirs under water-flooding sheds light for engineers to optimize development plans and better design remedial actions for enhanced water management. Although oil-well water production has been studied extensively, and hence different techniques have been introduced, only a few have addressed the source of produced water. Chan (1995 ) was among first to develop diagnostic plots to analyze the water-oil ratio (WOR). Chan plots (double-logarithms of WOR & its time derivative WOR’ vs. time) can, for example, indicate whether produced water comes through water coning or water channeling. Yet, these plots were obtained from conceptualized black-oil simulations which should be validated by more diverse field data. In this paper, Chan diagnostic plots were scrutinized using a suite of diverse field data that include permeability, production logs (PLT) and pressure transient analysis (PTA) from many vertical and horizontal wells with different completions. In this study, we demonstrated that more benefit of using Chan plots to diagnose water source can be gained through incorporating other field data into the analysis such as core data, PLT, and PTA. The diverse examples presented in this study revealed that near-well reservoir characteristics play a major role in shaping WOR & WOR’ plots rather than the well being whether vertical or horizontal. We suggest that, for a more accurate Chan analysis, only the production data at constant total liquid rate should be used. The second derivative of WOR may be utilized to identify the concavity of the WOR curve or the slope of the WOR’ curve.

High frequency asymptotic methods, based on solving the eikonal equation, are widely used in many seismic applications including Kirchhoff migration and traveltime tomography. Finite difference methods to solve the eikonal equation are computationally more efficient and attractive than ray tracing. But, finite difference solution of the eikonal equation for a point source suffers from inaccuracies due to singularity at the source location. Since the curvature of wavefront is large in the source neighborhood, the truncation error of the finite difference approximation is also significant, leading to inaccuracies in the solution. Compared to several proposed approaches to tackle source singularity, factorization of the unknown traveltime is computationally efficient and simpler to implement. Recently, a factorization algorithm has been proposed to obtain clean first order accuracy for tilted transversely isotropic (TTI) media. However, high order accuracy of traveltimes is needed for quantities that require computation of traveltime derivatives, such as take off angle and amplitude. I propose an iterative fast sweeping algorithm to obtain high order accuracy using factorization followed by Weighted Essentially Non-oscillatory (WENO) approximation of the derivatives. Although this method yields highly accurate traveltimes but it also results in increased computational load. Therefore, I propose a parallel fast sweeping algorithm to compute fast and accurate solution of the anisotropic eikonal equation. High accuracy is achieved first by using factorization followed by the WENO approximation of derivatives, whereas computational speed up is obtained by sweeping the computational domain in parallel. With a large number of CPUs, significant reduction in computational cost can be achieved for large 3D models. Numerical test shows improvements in accuracy of the TTI eikonal solution.

Side-mounted gun strings present a unique challenge for predictive perforation modeling tools because of their asymmetric geometry. To fully capture the dynamic response of the side-mounted system and more accurately predict the response of any gun system in general, it is important to fully capture the three-dimensional (3D) effects of model geometry and detonation-induced loading. This work details the modeling approach developed for a side-mounted gun system that enables the full geometry to be simulated so that accurate predictions of stresses and displacements could be made; these predictions are necessary for evaluating the damage potential to sensitive tools in the string, It is important to allow operation designers to optimize gun spacing and provide string flexibility to help ensure it can withstand downhole conditions without affecting performance. The simulation methodology was calibrated against previous test measurements, where loads and accelerations were captured during surface testing of a gun string. A detailed model was developed for the planned operation, and simulations were performed to predict the dynamic response of the wellbore fluid and tool string. Multiple damage sensitivities were identified for particular tools, and model results were extracted to evaluate 1) pressure dynamic loading on the tool, 2) displacement levels where movement is expected, and 3) dynamic loading of the tool. These results were provided to the developers of the sensitive tool to help assess potential damage risks. For each case, predictions were compared to previous test results and operation experience to develop a risk evaluation for the planned operation. Further, results were used to make adjustments to the operation to help optimize performance; comparison plots are presented for the different configurations evaluated. This overall process provided confidence to the operators that the operation would be performed successfully with no damage to the sensitive tool.

In naturally fractured carbonate reservoirs, Gas Oil Gravity Drainage processes (GOGD) are successfully implemented but oil recovery is limited by a slow kinetics. However a gas EOR process represents a promising alternative to boost this oil production rate. Nevertheless the design of this process should address several technical challenges: the typically unfavorable wettability of the matrix (intermediate to strongly oil-wet), the densely connected fracture network and the high contrast of fracture-to-matrix permeability. We propose here the injection of a advanced EOR foam with reduced interfacial tension. The foam flow in the fracture creates an important viscous drive leading to a pressure gradient, which increases the oil recovery dynamics compared to GOGD. Besides, the reduced interfacial tension (IFT) between crude oil and aqueous phase allows the aqueous phase to enter the matrix despite the unfavorable wettability. In this paper, we demonstrate that a balance exist between IFT and foam strength performances to optimize the process. Three foam formulations are optimized with very different profiles in terms of IFT and foam performances. For their design, priority is given either to ultra-low IFT values (10-3mN/m) or to a strong foam with larger IFT (0.35mN/m) or to a balance between the two first formulations (0.03mN/m). Foams are evidenced as intrinsically less stable in ultra-low IFT conditions: apparent viscosity (in porous media) in contact with oil is respectively enhanced by a factor 40 when IFT rises from 10 −3 to 10 −1 mN/m. Based on sandpack and coreflood experiments, we recommend an IFT in the order of 10 −1 mN/mas a balance between the viscous drive in fracture and an efficient aqueous phase imbibition in the oil-wet matrix. Simulation work supports this experimental conclusion: the common target of IFT in the order of 10 −3 mN/m determined by capillary desaturation curves in SP flooding can be adjusted to a higher IFT value, which can be deduced from the wettability of the reservoir. To ensure an accelerated oil recovery in naturally fractured carbonate reservoirs, we recommend the design of a low-IFT foam formulation with revised IFT performances compared to a classical Surfactant-Polymer process targeting residual oil. Indeed, the final process is likely more efficient if the target of IFT is defined by wettability requirements rather than residual oil desaturation. This article gives the target formulation parameters which arise from the mechanisms at play (viscous drive and imbibition in oil-wet matrix), and are realistically achieved with industrial surfactants.

The pulse fracturing is widely used in unconventional reservoirs. It alternately pulse pumping the proppant slurry and clean fluid to form discontinuous placement proppant pillars in the artificial fractures and the pulse fracture conductivity is several orders of magnitude higher than conventional hydraulic fracture conductivity. However, the understanding of the deformation law of proppant pillar under the action of closure pressure and proppant normal stress is unclear, resulting in difficult to calculate the fracture conductivity and prefer proppant. Firstly, replacement construction and experimental displacement by Renault Similarity Criteria, three typical proppant pillars placement structures are extracted through the large-scale visualized flat plate device. The Young's modulus of the proppant pillars are calculated in modified API conductivity cell. Secondly, proppant pillars are dispersed into particles by the Smooth Particle Method (SPH). Using the parameters obtain from the above experiments, fracture-proppant pillar contact models are established to simulate the deformation process of proppant pillar and get normal stress of proppant particles. Thirdly, extracting the shape of stabilized proppant pillars, establish the fracture-proppant pillar flow model, calculate the fracture conductivity in different closure pressure. The simulation results show that as the closure pressure increases from 14MPa to 41MPa, the fracture width present an accelerated downward trend, The fracture width under the support of the initial radius of 9 mm proppant pillars are the largest, decreasing from 2.52mm to 1.72mm, the larger the radius of the proppant pillar, the greater the fracture width, the normal stress of three types of proppant pillar particles are both changed from 73MPa to 110MPa. The elliptical cylinder proppant pillar has the largest fracture conductivity. Its fracture conductivity is reduced from 12500D•cm to 3630D•cm. The larger the construction displacement and the pulse time of proppant slurry, the greater the fracture conductivity. The model in this article can calculate the normal stress of proppant particle and fracture conductivity in different closure pressure, which can significantly guide the choice of construction parameters and the type of proppant.

Harvesting vast amounts of data has long been identified as an enabler of operational performance. The measurement of key performance indicators is a routine practice in well construction, but a systematic way of statistically analyzing performance against a large data bank of offset wells is not a common practice. The performance of statistical analysis in real time is even more rare. With the introduction of edge computing, devices capable of complex analytical functions in physical proximity to sensors and operations, this practice can be realized. Two case studies are presented: rate of penetration (ROP) and amount of vibration per run. Hypothesis testing is a statistical method in which a sampled dataset is compared against an idealized or status quo model. This model is built using many samples from a population. The characteristics of the population are then inferred from these samples. The model is built in centers where large amounts of data are available. These models are then transferred to an edge device in the field. The device collects real-time data and compares results to the status quo model. In the two cases presented, hypothesis testing was used to determine maximum and minimum levels of ROP and downhole vibration. This information is used to determine the effectiveness of new drilling practices, technologies, or methodologies. Because calculations are performed in real time, changes to drilling practices can be adopted quickly. The ROP case was performed at a US operating unit; the vibration case was performed in a Middle East unit. The models showed what improvement values should be. It was revealing to find wells that were thought to be poor performers were actually well within the population normal. Wells were also found that were thought to be good performers, but where new drilling methods were used, actually fell within the population model and thus suggested that the new methods had not affected performance. By performing this analysis on the edge device, operations can make changes early in such a way that results fall outside the status quo model and deliver real performance improvements. The paper presents the novel use of statistical models calculated in data centers in conjunction with real-time operations. Similar approaches in technical and physics modeling exist in which models are produced in the office and used in the field. However, building models for operations management, from a large bank of offset data, and performing analysis in the field with real-time data is a not common practice. This paper shows both technology and statistical methods that provide a valid scientific framework for operational performance improvement.

Carbonate reservoirs are widely spreaded in the world especially in China and middle east. Some of the carbonate formation is composed by small vuggs with radius of about or less than 1-meter, big caves with radius that can reach tens of meters, natural fractures and matrix. The current well testing models that assume dual or triple mediums cannot give reasonable interpretation such as the volume of caves and the connectivity between caves. Also, it’s hard to analyze the effect of caves’ morphology on flow and pressure dynamics. We proposed a model for the well connected to one cave in the carbonate formation based on the three laws of mass conservation, momentum conservation and energy conservation mechanics. The concept of wave propagation is introduced and the model incorporating fluid flow and pressure wave propagation is built in the caves. Three key parameters including the wave coefficient, damping coefficient, and cave shape factor are definied to characterize caves. Parametric study of the three parameters are conducted to analyze the effect of the caves on flow dynamics and bottomhole pressure transient. For the cave connected to the wellbore, its volume can be obtained by history match of the log-log plot of the pressure and pressure derivative. The matrix is considered as storage medium and it provides fluid to the caves by crossflow. Field cases are conducted to validate the proposed method. Results show there are four flow regimes in the log-log plot of the pressure and pressure derivative. The flow regimes include wellbore storage, transition flow from wellbore storage to cave storage, wave propagation and fluid flow in cave, formation flow. The four regimes can be used to characterize the features of fluid fow in carbonate reservoirs with caves. All of wave coefficient, damping coefficient, and cave shape factor present big influence on the bottom transient pressure and pressure derivative, especially in flow regime III which reflects the flow in caves. The volume of the caves connected to the wellbore for the field case is diagnosed. Both of the Log-log plots of the pressure and pressure derivative show good agreement with the pressure buildup data, therefore, the new method is validated and the results interpreted are accurate and confident.

Proppant transport adequately during hydraulic fracturing treatment assumes same perforation contribution through multi-perforation system. Proppant transport performance into the different ordination fracture system using multi-entry perforation technique is still not fully understood. This experimental study was aimed to deeply investigate five factors that affect proppant transport performance: number of perforations, perforation opening size, shear rate, fracture orientation, and proppant size distribution. The impact of these factors on proppant transport performance from different perspective was studied. Fracture slot model was designed and built to observe easily the effects of perforation density and fracture orientation. The results of this experimental work show that limited-entry perforation technique has significant impact on proppant transport within fractures where single top perforation had better proppant placement than multi-perforation system. Fracture area was approximately propped with 66% and 48% using top perforation and multi-perforation system, respectively. Slurry with high shear rate has a negative effect on the proppant equilibrium dune level (EDL) and fracture propped area (FPA). Fracturing treatment using high shear rate causes high pressure drop in the fracture that leads to decreasing EDL by 17% and fracture propped area by 23% comparing to using low shear rate. Using large proppant size (20/40) leads to form high EDL and FPA compared to 100 mesh size. Proppant transport dominated by four mechanisms and the vertexes near wellbore plays main mechanism to carry proppant farther inside the fracture.

Approximately 20% of all oilwells in the world use a beam pump to raise crude oil to the surface. The proper maintenance of these pumps is thus an important issue in oilfield operations. We wish to know, preferably before the failure, what is wrong with the pump. Maintenance issues on the downhole part of a beam pump can be reliably diagnosed from a plot of the displacement and load on the traveling valve; a diagram known as a dynamometer card. We demonstrate in this paper that this analysis can be fully automated using machine learning techniques that teach themselves to recognize various classes of damage in advance of the failure. We use a dataset of of 35292 sample cards drawn from 299 beam pumps in the Bahrain oilfield. We can detect 11 different damage classes from each other and from the normal class with an accuracy of 99.9%. This high accuracy makes it possible to automatically diagnose beam pumps in real-time and for the maintenance crew to focus on fixing pumps instead of monitoring them, which increases overall oil yield and decreases environmental impact.

Understanding the effect of injected water salinity is becoming crucial, as it has been shown to have a strong impact on the efficiency of oil recovery process. Various experiments have concluded that carbonate wettability is altered when the water ionic-composition is changed. In this work, a numerical investigation of an oil blob mobilized by water is conducted inside a single pore. The presented model studies the synergy effect of multiphase flow and water salinity at the pore level. To model the multiphase flow at the pore-scale, the full hydrodynamic Navier-Stokes equations are solved using direct numerical simulation (DNS). The effect of brine ionic-composition is examined through the electric double layer effect. Experimental zeta potential values, published in the literature, of crude oil/water and water/carbonate interfaces have been employed in the model, which capture the water salinity effect. The simulation results show that the water wetting film surrounding the oil-droplet collapses to an adsorbed nanometer water layer when high salinity water is used. As a result, a large pressure gradient is required to mobilize the oil inside the pore and overcome the attractive surface forces between the oil/water and water/carbonate interfaces. For low-salinity injected water, the carbonate surface becomes more water-wet. The wetting film surrounding the oil blob becomes stable due to the repulsive electric double layer force. Therefore, less energy is required to mobilize the oil blob inside the pore compared to high water salinity. The effect of solid roughness and injected water flow rate are also studied, which show to have a strong impact on the oil displacement efficiency. The novelty of the numerical method lies in efficiently capturing the nanoscale effect of the electric double layer in pore-scale multiphase flow at the microscale. The simulation results provide fundamental insights on the efficiency of low-salinity waterflooding at the pore level.

This paper presents the result of synergizing advanced well test deconvolution, rate transient analysis and detailed reservoir modeling to answer the compartmentalization uncertainty demonstrated by downhole pressure data in K field located offshore Sarawak, Malaysia. The gas reservoir is well distributed within approximately 10 × 20 km area and divided into two fault blocks, east and west. The development only focused at eastern area. Based on earlier assessment during Field Development Plan (FDP) stage, no fault has been seen from seismic data interpretation within east area. However, the six years actual performance of the field address different understanding which suggesting possible compartmentalization. Several assumptions were made to reconcile the connected hydrocarbon initially in–place estimated from conventional material balance and static volumetric calculation, however it has led to inconclusive results. The paper draws on the strength of an integrated reservoir engineering studies that include analytical and numerical analysis. The general framework covers advanced well test deconvolution analysis which multiple Pressure Build Ups (PBUs) and flowing duration were interpreted. This helps in guiding reservoir characterization understanding further from the well and type of fault setting within the reservoir. In addition, Rate Transient Analysis (RTA) has been conducted to estimate the connected volume. Several interesting findings will be shared in this paper as well. The combination of these deconvolution and RTA works enhanced the understanding of reservoir compartmentalization which guided the history matching process done through dynamic model. The newly established workflow has reduced the efforts required in history matching process and gave clearer picture of the uncertainty of reservoir compartmentalization.

Oil is considered one of the main drivers that affects the world economy and a key factor in its continuous development. Several operations are used to ensure continues oil production, these operations include; exploration, drilling, production, and reservoir management. Numerous uncertainties and complexities are involved in those operations, which reduce the production performance and increase the operational cost. Several attempts were reported to predict the performance of oil production systems using different approaches, including analytical and numerical methods. However, severe estimation errors and significant deviations were observed between the predicted results and actual field data. This could be due to the different assumptions used to simplify the problems. Therefore, searching for quick and rigorous models to evaluate the oil-production system and anticipate production problems is highly needed. This paper presents a new application of artificial intelligent (AI) techniques to determine the efficiency of several operations including; drilling, production and reservoir performance. For each operation, the most common conditions were applied to develop and evaluate the model reliability. The developed models investigate the significance of different well and reservoir configurations on the system performance. Parameters such as, reservoir permeability, drainage size, wellbore completions, hydrocarbon production rate and choke performance were studied. The primary oil production and enhanced oil recovery (EOR) operations were considered as well as the stimulation processes. Actual data from several oil-fields were used to develop and validate the intelligent models. The novelty of this paper is that the proposed models are reliable and outperform the current methods. This work introduces an effective approach for estimating the performance of oil production system and refine the current numerical or analytical models to improve the reservoir managements.