Abstract This paper presents the implementation of an integrated reservoir modeling approach that tightly connects and integrates different reservoir modeling disciplines. The approach allows the propagation of subsurface reservoir uncertainties across the various modelling domains, from seismic interpretation though to dynamic reservoir simulation and surface facilities modeling. The results achieved by this approach is a geologically consistent ensemble of runs (100 runs or more) that matches observed data and capable of predicting the future field performance under various development scenarios with a higher degree of reliability. Using an ensemble of runs that first honor the geological facies, as initially defined in the prior probability field, and subsequently updated by reservoir production behavior in the post probability field, enables the user to predict future field performance by allowing hydrocarbon recovery probabilities to be calculated, where the impact of subsurface reservoir uncertainties on prediction results and the possible risk in each development decision is estimated. The field studied is located Offshore in Abu Dhabi and has four main, two secondary, and few minor stacked carbonate reservoirs. Available evidence indicates that the field's reservoirs are not in communication and this study focuses on the main and secondary units. The conceptual geological model proposes that all the zones are conformable, with no truncation or pinching, forming a layer cake depositional model in which reservoirs range in thickness, from few feet in thin zones, to tens of feet in thick zones. Over time the field has been affected by different tectonic stress regimes, resulting in complex strike slip faulting, extending vertically across all reservoirs. Quantification of static and dynamic uncertainties together in the applied methodology is improving the team integration and common understanding for the field structural and geomodelling uncertainties and its impact on the dynamic model behavior for different reservoirs. The methodology has been tested where new drilled well data is included at different times and it showed an easy and quick update for the entire workflow from seismic to simulation. The model was calibrated to historical observed data using different geological uncertainties (structural, facies, geomodelling and dynamic uncertainties) which helped to achieve a geologically consistent and reliable models.

Abstract The purpose of this paper is to design a saturation height function which can overcome the measurement insufficiency and can also be applied to the reservoir models where core measurements are not available at all for all kind of siliciclastic reservoirs. This paper adopts a quantitative approach. The data is collected from the integration of core and log data. This approach is based on the assumption that sandstone reservoirs having similar Rock Quality Index (RQI), has similar capillary pressure behavior as regards water saturation. Universal capillary pressure curves were generated from 22 core plugs in Malaysia, using actual core data. Empirical relations between curve fitting parameters and core plug RQI values were developed. The universal capillary pressure curves were then used in other wells to calculate water saturation which has no core data. For those wells, the pseudo RQI values were calculated using the porosity and permeability derived from the log. A water saturation function was derived, as a function of RQI and HAFWL, with these pseudo RQI values and height above the Free Water Level. The results of this study portray that saturation modelled through this saturation height function matched very well with resistivity derived saturation. Water saturation resulting from generalized capillary curves was contrasted in wells without core data to that computed from resistivity logs. These water saturation results were found to be more realistic than those calculated purely from resistivity logs. The results confirmed the assumption that the universal capillary pressure curves can be used without core data to predict water saturation in wells. A well that has intercepted a hydrocarbon accumulation effectively represents a massive core sample of the reservoir. It is now possible to calculate the saturation reliably in any well. RQI is known over the entire hydrocarbon column. At the saturation anywhere in a reservoir can be determined, and the FWL known or estimated with reasonable certainty. It is in principle viable to evaluate Sw directly based on the drainage capillary theory. This study provides an insight into the universal saturation height function as a basic formulation in designing, developing, and appropriate strategies to model water saturation. This study not only helps subsurface study team in modelling water saturation, but it also offers new insights concerning the ability and capability of the strong direct relationship among rock quality index, height above free water level, and water saturation. This study uses an extended concept of Cuddy et al. (1993) to describe how water saturation varies with height above the free water level (FWL). Cuddy proposed the relationship of bulk Volume of Water vs Height above the FWL to derive water saturation. This approach requires conversion from BVW to SW. The proposed function, called K-function does not require the conversion.

Abstract This work investigates the implications of propagating the uncertainty and non-uniqueness of fitting experimental data into the forecast of field implementation of polymer flooding. This research sets the foundation for developing a general framework for modelling and probabilistic performance forecast of chemical flooding. History-matching of polymer coreflood results is posed as a bi-level optimization problem that solves for true waterflood residual oil saturation ( S orw , ∞ ) in the upper level and the relative permeability curves (exponents and endpoints of Corey function) and polymer adsorption parameters in the lower level under two different physical interpretations of the difference between remaining oil saturation after waterflood ( S orw ) and that after polymer flood ( S orp ): Displacement Efficiency Interpretation( S orp ≥ S orw ,∞ ) solely attributes the significant difference between S orw and S orp to improved displacement efficiency. Viscous Fingering Interpretation ( S orp ≤ S orw ,∞ ) considers viscous fingering during waterflood. A range of S orw ,∞ values has been identified for each model based on satisfying cut-off error criteria. For each S orw ,∞ value, dependent parameters are obtained and used in a simplified forecast of field performance in a quarter-five spot well pattern. The inverse of the error within acceptable range was used as probability density function (pdf) for S orw ,∞ which, combined with forecast results, enabled calculating P10, P50, and P90 of S orw ,∞ , recovery factor, and economic metric (i.e. cumulative discounted revenue). The probabilistic forecast results show that the viscous fingering interpretation is more reliable as it generates a forecast with less uncertainty. The impact of model selection and non-uniqueness of fitting lab data on the quantified uncertainty associated with field forecast is demonstrated and the premise of developing a bi-level optimization framework for modeling and probabilistic forecast of chemical flooding processes is supported by the findings of the study. In this work a novel iterative tangents construction technique that considers the inter-dependence between retardation factor due to adsorption and polymer shock saturation is used along with a mechanistic form of fractional flow curves that includes shear-thinning and effective viscous fingering effects. The analytical solution was verified against fine-grid simulation and the two solutions were nearly identical while the former is several orders of magnitude faster enabling extensive investigation of the range of parameters that match the coreflood results and forecast uncertainty under different technical and economic considerations.

Abstract Surfactants in literature have been reviewed and evaluated as foaming agents, but few of them fit our harsh operational and reservoir conditions. Therefore, surfactant formulations were tuned with guide of micelle electrostatic model and Winsor type phase behavior. The novel formulations are compatible with high temperature (94 ℃) and wide salinity (50 - 290 g/L). Moreover, one of the formulations can generate relatively weak foam at high residual oil saturation (Sor), but strong foam at low Sor. This auto-selection of foam by Sor allows to block the swept and clean zone, but not lockdown the virgin oil reserve and flooding front. At the end of foam flooding, almost 100% residual oil was recovered. Additionally, the operational conditions, such as injecting brines, anti-corrosions, demulsifiers, etc., are often subjected to be changed. Therefore, a rapid bulk-foam test is proposed to re-verify the compatibility of the formulation with the new operational condition.

Abstract The paper describes the planning and design of the first pilot well to unlock tight oil from a heterogeneous Jurassic carbonate reservoir, offshore Abu Dhabi. Reservoir permeability ranges from 0.1 to 5mD, with more than 80% of oil in-place locked up in the 0.1-0.3mD rocks. Conventional perforation, acid stimulation and zonal tests were seen to result in unsustainable flow and very low productivity index. Prior to committing capital investment, a decision was made to screen different completion and production technologies to exploit tight reservoirs, and deploy a pilot well to assess the feasibility of commercial recovery. The pilot study addressed the full value chain of upstream oil development, from static and dynamic modeling, drilling, completion and stimulation strategy, geomechanics and technology options screening including assessment of subsurface uncertainties, material requirements, and economics. Several well construction, stimulation and completion options were screened including stair-step horizontals, multilaterals, fishbone wells, multistage hydraulic fracking, stem tunneling and fishbone stimulation. Multistage acid frac was found to be technically and economically feasible offering improved recovery. Furthermore, this option is Abu Dhabi’s first ever offshore multi stage acid frac pilot, which is expected to lead and potentially unlock the rest of offshore tight reservoirs across the ADNOC Group.

Abstract In the subject reservoir, geological features (high perm streaks and fractures) are the key performance drivers that affect injected water movement behavior. However the horizontal and vertical distribution of these features have high uncertainties and it is very challenging to accurately capture these features and map into model. Here we developed one workflow to capture these features by integrating different sources of solid data, from core, open-hole resistivity log, PLT, well testing, production and surveillance data, etc. This reservoir has been on production for decades and plenty of static and dynamic data were acquired for the whole field. Among those data, conventional core analysis (CCAL), log derived k-PHIE regression, and well testing will measure reservoir permeability values, which could give guidance to the areal distribution of high perm and low perm regions. Afterwards, the difference between well test interpreted perm and CCAL measured perm could give some indication of some geological features (HKS, fractures, etc.) existing or not. Furthermore, these geological features could be further confirmed by the dynamic data, such as open-hole resistivity logs, PLT, and well production performance data, etc. After confirmation, those preliminary geological features will be mapped into static and simulation models, and the seamless geo-engineering loop of history match process will proceed until the final history match stage without compromising geologic plausibility and consistency. Going through the above integration workflow, the history match process was completed and the final history matched simulation model gives very promising water saturation match quality in both horizontal and vertical directions. In the horizontal direction, ∼90% of the producers get water cut match (trend and magnitude, dWC<5%), and also for recent drilled long horizontal producers (maximum reservoir contact (MRC) wells), model production profiles and water entry points along the laterals have good alignment with PLT and open-hole resistivity log data. In the vertical direction, model water saturation profiles have good alignment with open-hole resistivity log trend for hundreds of wells (∼90% matched). PLT and WFL also have very good match quality. In general this simulation model could reproduce water movement very well in three dimensions using this new integration workflow, with much improvement compared to current the model. It gives higher confidence in the model predictive capacity, and the future field development plan will be studied using this New Generation Model Based on this integrated approach, a more plausible and consistent set of geological features are mapped and thus achieve promising history match quality of water movement with minimum local modifications, and thus enhance the model predictive capacity and provide better support of the life cycle field development.

Abstract The main objective of this paper is understanding the impact of the fractal characteristics of fractured reservoirs on their pressure behavior, flow rate decline, and productivity index. The paper aims to propose a new methodology for developing several analytical models that could be used for describing the wellbore pressure behaviors at a constant sandface flow rate and the flow rate decline trends at a constant wellbore pressure. Considering the fractal characteristics in the proposed analytical models may help eliminating significantly the uncertainties in predicting reservoir performance in highly disordered and heterogeneous formations. The target of this study is the reservoirs characterized by a complex structure and undergoing an anomalous diffusing flow mechanism such as naturally fractured reservoirs with a nonuniform distribution of embedded fractures and depleted by multiple hydraulic fractures. The proposed methodology considers including the fractal characteristics such as the mass fractal dimension, conductivity index of anomalous diffusion flow mechanism, fractal-network parameters, fractional-derivative order, and matrix/fracture-interaction index as well as dual-porosity media characteristics such as the storativity and interporosity flow coefficient in the analytical models of the pressure, rate, and productivity index. These analytical models are derived for the porous media with the fractal networks of fractures that are embedded in the matrix and connected to hydraulic fractures. The porous media in this study are represented by two volumes. The first is the stimulated reservoir volume with a rectangular-shape area of a length equal to the hydraulic fracture length, while the second is the unstimulated reservoir volume that extends away from the tips of hydraulic fractures towards the reservoir boundaries. The solutions of the two approaches of the inner wellbore conditions are demonstrated in this study. The first is the wellbore pressure drop assuming constant sandface flow rate while the second is the decline rate and cumulative production assuming constant wellbore pressure. The outcomes of this study are: 1) Understanding the impact of the fractal characteristics on pressure behavior, flow rate declining pattern, and productivity index scheme during early and late production time. 2) Developing analytical models that consider the fractal characteristics of structurally complex reservoirs depleted by multiple hydraulic fractures. 3) The study has found that some of the fractal characteristics such as mass fractal dimension has a significant impact on reservoir performance while others such as matrix/fracture interaction index may not have a significant impact. 4) Fractal reservoirs exhibit better performance than the standard geometry reservoirs of single porosity media or naturally fractured reservoir with dual-porosity media. The novel point presented in this study is developing a new methodology for including the impact of the fractal characteristics in the pressure, rate, and productivity index models. In these models, the stimulated reservoir volume is assumed as a vertical wellbore with an equivalent radius equal to the fracture half-length. This approach is verified by comparing the results of pressure, rate, and productivity index obtained for a very short fracture half-length with the result of the vertical wellbore wherein an excellent matching is observed.

Abstract Gas injection is amongst the oldest and most used enhanced oil recovery (EOR) methods in the petroleum industry. Nevertheless, gas EOR is subject to poor macroscopic sweep efficiency due to the higher mobility and lower density of gas compared to reservoir fluids. Foamed-gas injection can regulate the mobility of gas by trapping a large fraction of the gas inside the porous medium, thereby increasing its apparent viscosity and reducing its relative permeability. However, the poor stability of foam at harsh reservoir temperature and salinity conditions is a major limitation that hinders the effectiveness of the foam flood. A combination of surfactant and nanoparticles (NPs) provides a novel solution to foam stability challenges. This study evaluates the role of NPs on enhancing foam stability. Static and dynamic laboratory tests were conducted along with particle size and zeta potential measurements to capture the foam stability and strength in porous media for a cationic surfactant combined with surface modified silica NPs. The static bulk foam stability was determined by measuring the foam half-life over time. The dynamic foam stability was determined through the mobility reduction factor (MRF) using a micromodel apparatus. The tests were carried out on a variety of NPs concentrations and fixed surfactant concentration. The results from the experiments show that the use of surfactant combined with NPs enhances the stability and strength of the generated foam when compared to the use of surfactant alone. The foam static tests show that the mixture of NPs and surfactants produces foams with smaller bubbles and longer half-life when compared to those in the absence of NPs. The results also demonstrate that the concentration of surfactant and NPs is a crucial parameter for foam stability and that there is an optimal concentration of NPs for strong foam generation. In porous media, the addition of NPs to surfactant solutions results in larger pressure differences across the micromodel chip and, accordingly, greater reduction of gas mobility when compared to those using surfactant solution alone. The results also reveal that the generation of NPs flocs is the main mechanism of foam stabilization enhancement. This work shows that using NPs at carefully selected concentrations in combination with surfactant can improve the foam static and dynamic stability in porous media, effectively lowering the mobility of the injected fluids and, eventually, improving the sweep efficiency of gas compared to the typical application of using surfactant alone.

Abstract SNOC embarked on an ambitious project to extract maximum value out of its mature fractured carbonate fields by converting them to underground gas storage facilities. This required the integration of a multitude of new and legacy data including new seismic acquisition, advanced processing and interpretation along with geophysical modelling, PVT, petrophysical and injection/pressure profiles to develop a robust reservoir model for the Moveyeid gas-condensate field. Enabling the operator to use this tool for advanced gas storage simulation predictions and to quantify risk in field development strategy to maximize condensate recovery while minimizing cushion gas volumes. Various seismic frequency cubes and survey vintages were used to delineate the Moveyeid structure and map top Shuaiba formation within the Thamama carbonates of Onshore Sharjah. Legacy wireline logs were reinterpreted using new multi-log workflows, providing inputs for static model development. As a mature field, in production for over 35+ years, cumulative produced volumes were used as an additional control, creating several iterations until achieving a match that honoured the geology. PTA, RFT and PLT were integrated in the model to quantify flow change over time. Advanced imaging revealed a north-South trending normal fault that segmented the field in to two, with all existing wells located on the up-thrown eastern block. Volumetric determination revealed that the eastern block alone was not sufficient to match hydrocarbons produced to date in any static scenario modelled. This was achieved with the inclusion of the western block and an unchanged gas-water contact. The new static model developed is more robust, with an enhanced layering configuration and property arrays that better reflect input data. Benefits were also seen during dynamic simulation where lower property multipliers were applied during history matching. Optimizing the layering and using block parallel computing power enabled the team to considerably reduce runtimes and produce an array of scenarios. The model was put to the test when the gas injection pilot project was commissioned in 2017 with low-pressure injection up to 2020, yielding a well performance accuracy within 5% of actual rates. Optimizing the strategy was essential in reducing planned cushion gas requirements by up to 20% for meeting the production target in the depleted reservoir, improving the project's capex. Utilising a combination of new and legacy data, a depleted gas-condensate carbonate reservoir has been successfully modelled. The model is being used as a tool to formulate the strategy and effectively define the field's suitability for gas storage and enhanced condensate recovery. This paper provides a case study for how these strategies can be implemented in other Middle Eastern analogues where gas storage in mature fields can act as a strategic tool for energy security.

Abstract Introduaction In a newly discovered field in the south east of Abu Dhabi, ten Thammama reservoirs were penetrated using a single well and how accurate an understanding of a reservoir and the uncertainties remaining can be assessed by a single well penetrating an accumulation of Hydrocarbon is a frequent question for companies planning to develop new, small reservoirs. The answer depends on how much data the single well got and the other information available about the reservoir and analogue reservoirs. When it comes to Modelling a reservoir that has only been pierced by one well, the assurance on the robustness of the available information is necessary in order to identify gaps and areas of uncertainty and then seek the means to minimize them. X-1 well drilled at the Crest of the structure and the location defined by utilizing leatest 3D seismic cube and the total depth 11500 ft RTKB. It was penetrated in the Lower Cretaceous sequence including ten reservoirs which are trapped by 4way dip closure. All the main reservoirs were tested in X-1 well and produced with an oil rate of 370 - 4,370 BOPD (30° - 32° API) An analytical "Lambda" Saturation Height Function was developed and implemented in order to overcome the absence of MICP data for initialization stage of Dynamic Modelling. Numerous choices made to develop a family of models that represents the range of reservoir possibilities. Uncertainty analysis including Analog Data from nearby fields utilized to rank the range of outcomes and prioritize obtaining additional information. This paper highlight a case study of a three onshore Abu Dhabi discovered structures addressing mitigations toward the challenges to draw a solid conclusion with single well modelling when it comes for development decision by Stakeholders at later stage. Further, the methodology as applied to the case study in this paper, allows identification of the range of outcomes and ranks the additional requirements, ranging from acquiring new Seismic data to drilling new wells, which guide for the next steps to a management decision to develop the reservoir or not.

Abstract Reservoir X is a giant oil reservoir of 30×10 Km 2 developed since 1973 using peripheral water injection. A major review study was done in 2016 highlighted major reservoir challenges that need to be managed and mitigated. The main challenges are (a) reservoir pressure decline in the central area (b) advancement of water cut/water conning (c) increased number of inactive strings (d) improper distribution of production offtake (e) inefficiency of current injection scheme. Consequently, reservoir deliverable potential has continuously dropped with increasing inactive strings threatening the attainment of the field mandated production target. This requires a major shift in our reservoir management strategies and best practices to cope with these challenges. In this paper, a case study for reservoir management in a mature carbonate reservoir with its detailed methodology is presented. An intensive and integrated technical work was conducted to (a) regain the target technical rate of 20% (300Mbpd) (b) arrest the pressure decline (c) implementation of a more efficient long-term plan to increase reservoir pressure and to maximize ultimate recovery. A detailed approach was developed which consisted of short, mid and longterm actions. This approach involves (a) re-prioritization of infill drilling through weighted ranking (b) pressure recovery implementation strategy through sectorization, re-balancing of production offtake and improving water injection efficiency (c) sector based evaluation using simulation/streamlines/data analysis for the voidage replacement (e) water injection optimization with the help of new peripheral water injectors, increased water injection availability (New clusters and network debottlenecking) and new Water Injection scheme (Mid Flank water injectors) (f) tracking the implementation of the Pressure Recovery Plan with a dedicated and tailored monitoring dashboard. Through this integrated approach a portfolio of wells is selected and ranked that help in achieving the objectives of the reservoir development efficiently and the 20% Technical Rate achievement. The sector wise analysis and tracking approach enabled in arresting the pressure decline in the crestal area and ensured that the focus remained on the recovering pressure in the worst affected areas of the reservoir while meeting the production requirements. Another important outcome of this work was to drive a slew of initiatives related to the improvement of water injection efficiency that is a necessary requirement to recover the pressure in the crestal area. This reservoir management methodology and framework put in place provides a platform to continuously integrate the inputs (production/injection; VRR; pressure) and develop understanding from the different disciplines of the reservoir development to monitor and manage the reservoir in and efficient and timely manner.

Abstract Acquiring reservoir fluid samples through formation testers is critical to asset evaluation in most oil and gas drilling operations. From the time this technology was introduced to the industry, the key challenges have been in planning the job, estimating contamination during operation, and obtaining clean fluid samples in the shortest time possible. The objective of this paper is to create a new data-driven model to proactively simulate the cleaning process in order to provide a practical job-planning tool that optimizes fluid sampling. After detailed analysis of formation pump-out cleaning behavior and oil-well sampling, a parametric study with nearly a hundred thousand scenarios was designed to model fluid behavior during sampling. The simulation scenario is a multi-component model with radial geometry, capable of handling complex reservoir rock, fluid composition, probe geometry, and sampling conditions. Compositional simulation output is then used to generate the comprehensive database of the fluid sampling and cleaning processes. The study is used to determine the sensitive parameters related to sampling and contamination. Full factorial experimental design was used to build nearly one hundred thousand scenarios with more than 10 relevant parameters. Outputs were analyzed through a variety of visualization and statistical techniques to understand cleaning behavior in different initial and operating conditions. One-factorial analysis and statistical tests, including analysis of variance (ANOVA), were used to determine the significance of the different parameters. The most influential parameters have been selected and used as input to the representative model in order to predict pumpout volume and corresponding contamination. In this work, multiple data-driven models such as Neural network, Random Forest, and Gradient Boosting are presented. Furthermore, multiple mathematical equations have been compared to fit the contamination trend, and methods of estimating their best fit parameters are presented. Blind testing has been performed to evaluate performance of the developed models, showing promising results. The workflow, database, and the developed models can be used to perform forward modeling of sampling jobs in different reservoirs, drilling muds, and operating conditions for both wireline and logging while drilling (LWD). This enables an effective and practical job-planning tool implementation, whereby a tool string can be optimized to reduce sampling time while improving the quality. The state-of-the-art workflow deployed in a commercial reservoir simulator combines physics, programming, statistical analysis, and machine learning techniques to tackle the challenging problem of sampling. The workflow and data can be used during operations with various wireline formation testing (WFT) and LWD testing tools to optimize cleanup and sampling of formation fluids. Simulations of different realizations of reservoir properties, drilling mud invasion profiles, and cleanup operations also helped develop a useful and diverse pumpout database.

Abstract Permeability prediction is an important step in reservoir characterization for effective modeling of water-flood performance and subsequent EOR processes. Permeability should be determined accurately versus depth in all cored and logged wells in a field. This is normally achieved by the use of cross-plots of core permeability versus core porosity from log-derived porosity in un-cored wells. The main aim of this research is to use X-ray CT images of reservoir cores to identify geological textures and predict permeability based on well-established models in carbonates. Full-diameter reservoir cores were imaged using dual energy X-ray CT. Porosity logs were derived along the entire cores from the dual energy data. Textural variations were identified from the appearance of the CT images. Statistical sample selection was designed to honor the distribution of porosity and CT-textures. Porosity-permeability correlations were established in the texture domain using plug-scale poroperm data and thin-section photomicrograph analysis. Plug-scale geological analysis confirmed the CT textures in the images, which allowed the derivation of texture logs along the cores. The porosity and permeability data were fitted into unique trends that were derived from the detailed textural analysis. This process provided the link between the poroperm trends and the different textures in the core enabling permeability to be predicted along the entire whole core intervals. This paper describes a novel approach of combining textures with porosity to model permeability along the reservoir column. A unique dual energy CT technique was used to ensure that all the core property variations were well represented in the plug-scale core analysis measurements. This analysis will open the way for predicting permeability in un-cored wells using texture information from down-hole image logs. In the future, machine-learning algorithms will be employed following the same workflow for effective permeability prediction in the field.

Abstract This study attempts to describe and model the process leading to the genesis of the tilted oil-water contact (OWC) observed in the lower part of the Thamama Group in an offshore Abu Dhabi field. Post-oil-migration deformation is thought to be the mechanism that produced a tilted OWC dipping towards the Northeast. Deciphering the tectonic evolution from Jurassic to Paleocene times confirms a long and complex structural history combining burial, halokinesis, uplift and tilting. Diapiric activity was probably established in the eastern accumulation by pre-Jurassic times, followed by localized salt-related doming in both parts of the field. During the mid-Eocene occurred a late tilting of the northeastern part of the field, enhancing the curvature of the area. This late tilting caused oil saturation redistribution. In uplifted areas of the field, water saturationdecreased along the drainage curve whereas in areas brought structurally closer to the Free Water Level (FWL), water saturation increased along a scanned imbibition curve. The objective of this study is to retrace the saturation history of the field using lab-measured bounding capillary pressures. This workflow ensures the correct initialization of the dynamic reservoir model and reproduces the observed field behavior. Drainage and imbibition capillary pressures are available for different rock types (RT), measured under various experimental set-ups (mercury injection, porous plate, centrifuge). This study reconciles lab measurements with wireline logs and Dean-Stark data to produce a representative capillary pressure curve for each RT. Next, the structural deformation history is representedas a series of elementary geometric transformations (localized subsidence and global translation) to restore the reservoir in its pre-deformation state. Wireline log saturationsare matched to capillary-based water saturations by adjusting the present day free water level (FWL) and the change of FWL due to seepage. The dynamic model is then initialized by enumeration with the original water saturation and let to equilibrate for 40,000 years. The fluid redistribution and pressures are then monitored to confirm that equilibrium has been attained. This equilibration step ensures that the fluids are at their correctstate of relative permeability and capillary pressure at the start of simulation, something that is not garanteed in the case of direct enumeration of the final saturations. The implications of such procedure on the dynamic behaviorare explored by simulating 50 years of production history and compa. This study greatly improved the saturation modelling by moving from synthetic porosity-bin functions to physics and texture based capillary pressures. The proposed workflow enhanced the history-match quality and reproduced observed field behaviors such as the high water-cut development in the Northeast. A tilted OWC might increase the in-place however extracting those resources might prove more challenging in the face of the low oil mobility. The oil below OWC might not be recovered under conventional waterflood methods and would warrant an EOR implementation. In the future, an appraisal well is planned in the Northeast to assess the volume and mobility of the oil below OWC. It is the first time an integrated workflow, combining SCAL and structural geology, is proposed to correctly initialize the dynamic model for reservoirs that experienced a post-migration deformation, hence making the present study unique.

Abstract A Reservoir Rock Typing (RRT) and Saturation modelling integrated study of the Lower Cretaceous Group reservoirs in Field X was conducted as part of required inputs into an ongoing FDP update. The results of this study allowed for a robust 3D modelling workflow that included saturation height modelling of the observed tilted oil water contact in the acquifer underlying three (3) of the major hydrocarbon bearing zones. The study intervals covered intervals that for the purpose of this paper will be called XH-4, XH-1, KX-2, XK-3 and XK-2 zones. Rock types were defined using core data integrated with geological descriptions and 3D distributed taking into consideration the depositional environments. Porosity, permeability and saturation property distribution was guided by the RT distribution. In total 44 wells in the field plus 4 nearby wells were incorporated in this study. This paper analyses the petrophysical, geological and static modelling workflows utilized. The RRT definition was based on the prediction of Self Organising Map (SOM) electrofacies which were classified using the petrophysical grouping obtained from lithofacies and depositional environment, permeability and MICP data. As a further step, the predicted RRT was optimised where the Archie saturation indicated that a different rock type was more likely. The analysis of virgin formation pressure data to understand the tilted OWC/FWL concept reveals that different wells have different absolute pressures. When the differences are mapped no regional trends were observed which makes a hydrodynamic origin of the tilted OWC/FWL less likely. Free water level (FWL) has been interpreted on well by well basis and mapped across the field for a better understanding of the variation. The pressure data are consistent with the scenario that the XH-4, XH-1 and KX-2 zones all share the same free water level for modelling purposes. The OWC is more or less flat in the central field area and is about 35 ft deeper in the eastern part of the structure. Although the difference in OWC could be abrupt (compartmentalisation), a gradual tilt seems to fit the data better. Furthermore, the ‘deepest oil observations’, which relate to paleo conditions, are deeper than present-day FWL in the east. All these observations would be consistent with a structural tilt towards the east. Similar trends were also observed from the FWL interpreted across key wells having formation pressure data. In terms of reservoir quality, the interval XK-3 has highest porosity and highest permeability. Also XH-1 and KX-2 contain good reservoir quality rocks. XH-4 is located above XH-1 and KX-2 and has relatively low quality matrix properties. It does contain low porosity (and hence brittle rocks) that can be expected to contain oil when fractured. The deeper XK-3 is probably disconnected from the underlying XK-2 which can be explained by the argillaceous interval between XK-3 and −2. XK-2 consists of thin reservoir intervals so that both Archie saturation estimates and the rock typing work carry significant uncertainty. The derived saturation height (SH) models utilise both a present-day and a paleo free water level per well. Each reservoir interval has its own SH-model based on SH-functions for each RRT. Implementation of all defined RRTs with associated SHFs was done to allow for highest resolution population of the geomodel, though a significant number of algorithms is required for this process. The RRT population of the static models was designed to be obtained from the combination of 3 distributed parameters: porosity, depositional environment, and reservoir quality by using the algorithms provided. Reservoir Quality is the driver most closely linked to diagenesis and local permeability variations.

Abstract To effectively simulate the performance of unconventional wells, it is essential to incorporate sufficient geological complexity to allow for realistic variability in the petrophysical and mechanical properties controlling the productivity of the effective stimulated rock volume. The heterogeneous and strongly layered nature of unconventional reservoirs requires appropriate representation of the high intra-bed contrast in anisotropic deformation and flow behavior, and the representation of pre-existing mechanical discontinuities (faults, bedding planes and natural fractures) in terms of mechanical and hydraulic coupling. These fundamental requirements cannot be achieved unless a high-vertical resolution petrophysical and geomechanical model is developed. As demonstrated in this paper, without integration of high (cm-scale) vertical resolution data such as borehole images and core X-ray computed tomography (CT) images, standard meter-scale petrophysical and geomechanical outputs typically used for Hydraulic Fracture (HF) and flow modeling are, at best, standard scalar log (~m-scale) average compositional volume-weighted representations of subsurface reality, that can be highly misleading when considering that mechanical and flow properties are actually ultimately controlled by fine-scale contrasts and extremes, rather than by arithmetic composition weighted averages. This paper presents an integrated workflow to model mechanical properties at sufficiently high resolution (cm-scale) to accurately honor rock fabric and its effects on HF (height and complexity) and therefore on production. The workflow relies on (1) a novel experimental geomechanics technique and associated analytical solutions to derive poroelastic anisotropy (Young's modulus, Poisson's ratio and Biot's coefficient in the bed-normal and bed-parallel directions) and (2) a joint, high vertical resolution, multi-physics petrophysical model relying itself on (2.1) a standard vertical (m-scale) resolution multi-physics log-based petrophysical output (average composition) automatically redistributed into a high vertical resolution well framework by using (2.2) local constraints from high-resolution data (borehole resistivity image and core dual energy X-ray CT images) coupled with neuronal-network derived core empirical relationships. Core-scale poroelastic predictors defined in (1) are then propagated into (2) to get high vertical resolution geomechanical properties and sub-surface stresses. Beyond providing appropriate inputs to HF modeling, this high vertical resolution well framework enables (i) detailed well-scale calibration and recognition of facies and stacking patterns; (ii) accurate and core-calibrated geochemical, petrophysical and geomechanical characterization of individual beds, and (iii) identification and characterization of discontinuities, in general, and interfaces between beds, in particular. Once upscaled, outputs of this workflow enable a more realistic borehole-view of reservoir quality, fluid flow units and geomechanical stratigraphy – all key information to a more optimal asset development.

Abstract The initial hydrocarbon saturation has a major impact on field development planning and resource estimation. However, it is derived from indirect measurements from spatially distributed wells applying saturation height modelling based on uncertain parameters. The methodology presented here is deriving posterior parameter distributions by using Machine Learning in a Bayesian Framework honouring the petrophysical uncertainty in the field. The results are used for initialization and will be applied for forecasting under uncertainty. To determine the initial hydrocarbon saturation, the Saturation Height Model (SHM) needs to be conditioned to the interpreted logs. 2500 geological realizations were generated to cover the interpreted ranges of porosity, permeability and saturations for 15 wells. For the SHM, twelve parameters and their ranges were introduced. Latin Hypercube Sampling was utilized to generate a training set for Machine Learning models (ML). The trained ML models were conditioned to the log derived saturation data. To ensure a field wide consistency of the models only parameter combinations honouring the interpreted saturation range for all wells were selected. The presented method allows for consistent initialization and for rejection of parameters that do not fit to observed data. In our case study, the most significant observation concerns the posterior parameter distribution of the Free Water Level range which is narrowed down. Furthermore, the SHM parameters are proven independent; thus the resulting posterior parameter ranges for the SHM can be used for conditioning production data to models and subsequent hydrocarbon production forecasting. Additional observations can be made from the ML results such as the correlation between wells, this allows interpreting groups of wells that have a similar behaviour, favour the same combinations and potentially belong to the same compartment. A workflow is introduced honouring the interpreted error of log derived porosity-permeability and saturation data. The workflow is based on Saturation Height Modelling (SHM) to generate a training set for Machine Learning. The SHM is conditioned to all wells in a Bayesian Framework to ensure that the posterior parameter distributions of the SHM are consistent and can be used for conditioning dynamic models to dynamic data and production forecasting.

Abstract Closed loop reservoir management is challenged with building reliable and fast predictive reservoir models to make field decisions. Traditional numerical simulation models can be difficult to characterize, tedious to build and calibrate, and at times computationally prohibitive for short term decision cycles in field applications. On the other hand, pure data-driven methods often lack physical insights and have limited range of applicability. For operational scenarios such as short-term production forecasting, waterflood optimization, production control and understanding major reservoir mechanisms, it is desirable to use a reservoir modeling methodology that is easy to build, history match, compute and interpret. In this work, we propose to use a hybrid and efficient reservoir graph network (RGNet) modeling approach based on time of flight concept that can be built using routinely measured field measurements (such as pressure and rates) and can be used for real-time forecasts, scenario modeling, production optimization and control. We propose a gridding method based on discretized time of flight for multi-well scenario with interference. It simplifies the 3D reservoir flow problem into a graph network representation that can be solved with any commercial reservoir simulator, which enables the RGNet model to be readily applied for various types of fluid physics. The parameters in RGNet model are obtained through assimilating observed data. The RGNet model has a very compact model representation that requires significantly less complexity compared with full-physics 3D models, which leads to very fast simulation. The efficiency of RGNet makes it appealing for applications where many simulation runs are needed. We applied the proposed approach on SPE benchmark reservoir simulation models for single well, multi-well with interference and injector-producer pairs. The calibrated models are used to quantify uncertainty for production forecasting. In all cases, the range of uncertainty is reduced effectively and efficiently with data assimilation. The posterior RGNet models are also shown to provide reasonable estimates of reservoir and well drainage volumes. By virtue of the reduced complexity, the modeling methodology is highly scalable while still retains physical interpretability (in terms of pore volume and transmissibility). We also discuss the potential applications of the method such as reservoir connectivity analysis and well control optimization. The proposed reservoir graph network (RGNet) modeling approach provides a unique and sustainable way to combine advanced analytics and physics to develop an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.

Abstract Many horizontal wells in GCC (Gulf Cooperation Council) are producing from fractured carbonate reservoirs under aquifer pressure support. After water breakthrough, these wells suffer from high water cut rise due to water channeling through the fractures, inducing a strong and quick loss of well productivity. The wells being completed horizontal open hole, zonal isolation is very difficult to isolate the water producing zone. Moreover, mechanical water shut off like using liners or open hole packers is normally costly and challenging as well. Therefore, bullheading chemicals is often the only remaining option. The fractures being producing mixture of both oil and water, water shutoff in this type of well is very challenging. To solve this problem, an original method was used consisting of microgel and gel injections. The strategy is to create a flow barrier deep in the fracture while preserving oil production from the matrix. The optimized procedure enables control of chemical placement by bullhead injection. Laboratory tests were conducted to optimize Microgel and gel chemistry (injectivity, gelation time and gel consistency/stability, return oil permeability). The treated candidate was 530m long horizontal well and produced at around 600 bpd with water cut of 92%. Temperature and salinity were 62°C and around 100,000ppm respectively. Well behavior indicated existence of fractures along the wellbore. The treatment consisted of 230m3 of successive Microgel/Gel injection at an average rate of 2.5 BPM. The treatment proceeded bullhead through annulus. WHP remained low throughout injection (150 psi). After curing time of 10 days for gel set, production was released with ramp up rate. The well, which was facing strong production decline, stopped declining, with a water cut drop of around 10%. Incremental daily oil rate was around 50 bopd with cumulative oil for the first 4 months of around 4500 bbls of oil. This technology may have many applications in fractured carbonate fields and may lead to better oil potential from such high water cut highly fractured reservoir wells, with strong aquifer.

Abstract Since its discovery at 1971, numerous matrix stimulations were performed in South Pars field. However, there are still various challenges on stimulation job design and evaluation methods. In order to tackle these issues, fifteen matrix operations were selected to be analyzed from different phases of development project of the reservoir. The objective of this study is to introduce a new approach to figure out optimum acid volume (gal/ft), compare the effectiveness of diverters, calculate stimulation ratio (SR), and forecast post-acid production behavior from surface treating data. Modified inverse injectivity (I inv ) method, which is fully discussed by Safari et al. (2020) , is utilized in this study. Obtained data were analyzed in terms of I inv decreasing trend, I inv humps as well as pre/post-acid I inv during stimulation process. In addition, pre/post-stimulation surface testing data are gathered and analyzed. These data are coupled with post-acid I inv to find a correlation to predict production behavior of treated wells. SR is defined as the ratio of pre-acid I inv to post-acid I inv of a treated well. Finally, SR values are validated with available PLT data of two stimulation operations. Obtained results indicate that optimum gal/ft of acid depends on well conditions. In this regard, wells with high initial formation damage require more volumes of acid. In this regard, wells treated with 27/27 gal/ft design (27 gal/ft 28%HCl and 27 gal/ft 15%VES, i.e. Visco Elastic Surfactant,) were under-stimulated. Although, 53/53 gal/ft design seems to be adequate for low-skin wells, higher gal/ft would further enhance productivity of highly damaged wells. This result was confirmed by stimulation of a damaged well with 60/60 gal/ft design. Finally, the most reliable design applied in the field is 70/70 gal/ft so far. I inv analyses depict that better diversion is observed in wells with lower injectivity and higher damage. Generally, the magnitude of Iinv humps, which represents as a supporting evidence for diversion, decreases as stimulation proceeds. Furthermore, fiber–laden VES diverter exhibited better diversion than VES alone. Surface post acid drawdown at a specific production rate, e.g. 50 MMSCFD, is defined as the difference between shut-in and flowing wellhead pressures. Obtained results demonstrate that there is a direct relation between the post-acid Iinv and surface drawdown in this field. Therefore, production behavior of treated wells can be correlated by having access to post-acid Iinv. Ultimately, calculated SR values demonstrated average deviation of less than 10% with downhole PLT data. The novelty of this work pertains to utilization of the I inv method to evaluate performance of matrix stimulation operations. By applying this method, optimum volume of acid and diverter, diversion effectiveness, stimulation ratio as well as a prediction of post-acid surface drawdown can be obtained from surface treating data. The proposed methodology led to get better understanding of South Pars reservoir behavior during matrix stimulations. The same methodology can be applied to other carbonate reservoirs, too.