Reservoir Engineering Studies Research Articles

Methods Used to Estimate Reservoir Pressure Performance: A Review

Reservoir pressure plays a significant role in all reservoir and production engineering studies. It is crucial to characterize petroleum reservoirs: by detecting fluid movement, computing oil in place, and calculating the recovery factor. Knowledge of reservoir pressure is essential for predicting future production rates, optimizing well performance, or planning enhanced oil recovery strategies. However, applying the methods to investigate reservoir pressure performance is challenging because reservoirs are large, complex systems with irregular geometries in subsurface formations with numerous uncertainties and limited information about the reservoir's structure and behavior. Furthermore, many computational techniques, both numerical and analytical, have been utilized to examine reservoir pressure performance. This paper summarizes the concepts and applications of traditional and novel ways to investigate reservoir pressure changes over time. It provides a comprehensive review that assists the reader in recognizing and distinguishing between various techniques for obtaining an accurate description of reservoir pressure behavior during production, such as the reservoir simulation method, material balance equation approach, time-lapse seismic data, and modern artificial intelligence methods. Thus, the central concept of these procedures and a list of the authors' research are discussed.

Relative permeability estimation using mercury injection capillary pressure measurements based on deep learning approaches

Relative permeability (RP) curves which provide fundamental insights into porous media flow behavior serve as critical parameters in reservoir engineering and numerical simulation studies. However, obtaining accurate RP curves remains a challenge due to expensive experimental costs, core contamination, measurement errors, and other factors. To address this issue, an innovative approach using deep learning strategy is proposed for the prediction of rock sample RP curves directly from mercury injection capillary pressure (MICP) measurements which include the mercury injection curve, mercury withdrawal curve, and pore size distribution. To capture the distinct characteristics of different rock samples' MICP curves effectively, the Gramian Angular Field (GAF) based graph transformation method is introduced for mapping the curves into richly informative image forms. Subsequently, these 2D images are combined into three-channel red, green, blue (RGB) images and fed into a Convolutional Long Short-Term Memory (ConvLSTM) model within our established self-supervised learning framework. Simultaneously the dependencies and evolutionary sequences among image samples are captured through the limited MICP-RP samples and self-supervised learning framework. After that, a highly generalized RP curve calculation proxy framework based on deep learning called RPCDL is constructed by the autonomously generated nearly infinite training samples. The remarkable performance of the proposed method is verified with the experimental data from rock samples in the X oilfield. When applied to 37 small-sample data spaces for the prediction of 10 test samples, the average relative error is 3.6%, which demonstrates the effectiveness of our approach in mapping MICP experimental results to corresponding RP curves. Moreover, the comparison study against traditional CNN and LSTM illustrated the great performance of the RPCDL method in the prediction of both So and Sw lines in oil–water RP curves. To this end, this method offers an intelligent and robust means for efficiently estimating RP curves in various reservoir engineering scenarios without costly experiments.

Selection of a Dimensionality Reduction Method: An Application to Deal with High-Dimensional Geostatistical Realizations in Oil Reservoirs

Summary One of the challenges related to reservoir engineering studies is working with essential high-dimensional inputs, such as porosity and permeability, which govern fluid flow in porous media. Dimensionality reduction (DR) methods have enabled spatial variability in constructing a fast objective function estimator (FOFE). This study presents a methodology to select an adequate DR method to deal with high-dimensional spatial attributes with more than 105 dimensions. We investigated 18 methods of DR commonly applied in the literature. The proposed workflow accomplished (1) definition of the adequate number of dimensions; (2) evaluation of the time spent for each data set generated using the elapsed computational time; (3) training using the automated machine learning (AutoML) technique; (4) validation using the root mean square logarithmic error (RMSLE) and the confidence interval (CI) of 95%; (5) a score equation using elapsed computational time and RMSLE; and (6) consistency check to evaluate if the FOFE is reliable to mimic simulator output. We used FOFE to generate risk curves at the final forecast period (10,957 days) as an application. We obtained methods that reduced the high-dimensional spatial attributes with a computational time lower than 10 minutes, enabling us to consider them in the FOFE building. We could deal with high-dimensional spatial variability from those selected approaches. Moreover, we can use the DR method selected to deal with high complexity problems to build an FOFE and avoid overfitting when a massive number of data are used.

Evaluating geomechanical effects related to the production of a Brazilian reservoir

This paper presents a coupled finite element approach for modeling geomechanical effects induced by production/injection processes in petroleum reservoirs. The module developed employs coupled- reservoir analysis using CMG IMEX® as the flow simulator and a finite element program in MATLAB® as the stress–strain simulator, in a two-way explicit partial coupling scheme. The flow and mechanical problems are coupled by the change of effective stress due to the change in pore pressure and by varying stress-dependent reservoir properties, such as pore compressibility, absolute permeability, and porosity. The coupling procedure was applied to the Namorado Field (Campos Basin, Brazil) to quantify the impact of the rock deformation on fluid recovery. Based on the cases studied, the coupled analyses predicted higher oil recovery than the conventional reservoir simulations. The results showed that the reservoir deformation can affect its performance and must be taken into account in reservoir-engineering studies depending on production strategy and reservoir stiffness. Besides, the geomechanical calculations were performed only in the coupling timesteps, reducing the computational effort and making this coupling method feasible on a field scale.

Improving coarse-scale simulation models with a dual-porosity dual-permeability upscaling technique and a near-well approach

In the management of an oil field, the creation of a highly detailed fine-scale model to properly represent the key reservoir heterogeneities is important. This model better represents the main reservoir features but remains a significant challenge for dynamic reservoir simulations with impractically high computational costs, even for single numerical simulation runs. Therefore, the scaling up of a highly detailed model is a crucial step in reservoir engineering studies to enable field-scale simulation runs within a time-frame compatible with the needs of the industry. Upscaling must be carefully performed because it can introduce significant errors and bias to the numerical models due to truncation errors and the smoothing of sub-grid heterogeneities.This work focuses on improving the representation of small-scale heterogeneities in coarse models. It expands the dual-porosity and dual-permeability upscaling technique proposed (Rios et al., 2020) to be applied to 3D highly heterogeneous systems. Specific near-well treatment is considered to improve the overall flow distribution in the wells; with this procedure, the coarse model better represents the production and injection profiles throughout the wells. Therefore, the final coarse-scale model can represent the main reservoir heterogeneities and possible preferential pathways, while improving the definition of the wells’ productivity and injectivity indices.The proposed upscaling technique is applied to different reservoir simulation models with immiscible water flooding in 3D evaluations. Our approach was validated by two case studies: SPE-10 and a field-case heterogeneous reservoir with karst structures. The focus is to demonstrate that the division of the porous media into primary and secondary systems improves capillary and gravity representation of coarse models in 3D applications, while the near-well upscaling makes the flow distribution in the wells in agreement with the fine-scale solution. The resulting coarse-scale dual-permeability models are much faster than the reference fine models (between 0.025% and 1.5% of the fine-scale simulation time), more accurate than the traditional flow-based upscaling results, and can better reproduce the fine-scale responses in different upscale ratios (from 25 up to 1 700).

Practical workflow to improve numerical performance in time-consuming reservoir simulation models using submodels and shorter period of time

Numerical reservoir simulation is a valuable tool to support the decision-making process in oil field projects. For this purpose, current reservoir engineering studies request numerous simulations runs for complex workflows, such as numerical reservoir characterization, data assimilation process, strategy optimisation, well placement studies, production management and supplementary project support. Thus, providing efficient and effective simulation numerical models is a critical task in reservoir simulation studies before starting the daily run demands.In this work, we propose, test and evaluate a practical workflow to improve the numerical performance of time-consuming reservoir simulation models. We provide a procedure to select representative numerical submodels and representative snapshots interval from simulation time (RSIST) to reduce the total time spent in numerical optimisation. The first approach allows the optimisation of the numerical parameters without the drawback of simulating long execution runtime. The second enables to select a range of simulation time where typical convergence problems of timestep cuts occur. The numerical parameters are then optimised using submodels and RSIST, where we highlight a workflow to reduce runtime of the optimisation of slow reservoir models. Following, the resultant set of parameters is applied to the entire (original) reservoir numerical model, improving its performance and allowing more effective execution of several simulation-runs in reservoir engineering studies.To conclude, we provide a workflow to be applied to any reservoir simulation model, especially those with complex structural grid and high-time consuming to run. Our results demonstrated remarkable improvements in the numerical performance of simulation models, with a high potential of saving days of work during probabilistic evaluations. Furthermore, we recommend this workflow as a first step before starting studies using reservoir simulation to avoid unphysical and time-demanding simulation runs that may affect future decisions.

Upscaling Technique for Highly Heterogeneous Reservoirs Based on Flow and Storage Capacity and the Lorenz Coefficient

Summary Field-scale representation of highly heterogeneous reservoirs remains a challenge in numerical reservoir simulation. In such reservoirs, detailed geological models are important to properly represent key heterogeneities. However, high computational costs and long simulation run times make these detailed models unfeasible to use in dynamic evaluations. Therefore, the scaling up of geological models is a key step in reservoir-engineering studies to reduce computational time. Scaling up must be carefully performed to maintain integrity; both truncation errors and the smoothing of subgrid heterogeneities can cause significant errors. This work evaluates the latter—the effect of averaging small-scale heterogeneities in the upscaling process—and proposes a new upscaling technique to overcome the associated limitations. The technique is dependent on splitting the porous media into two levels guided by flow- and storage-capacity analysis and the Lorenz coefficient (LC), both calculated with static properties (permeability and porosity) from a fine-scale reference model. This technique allows the adaptation of a fine highly heterogeneous geological model to a coarse-scale simulation model in a dual-porosity/dual-permeability (DP/DP) approach and represents the main reservoir heterogeneities and possible preferential paths. The new upscaling technique is applied to different reservoir-simulation models with water injection and immiscible gas injection as recovery methods. In deterministic and probabilistic studies, we show that the resulting coarse-scale dual-permeability models are more accurate and can better reproduce the fine-scale results in different upscaling ratios (URs), without using any simulation results of the reference fine-scale simulation models, as some of the current alternative upscaling methods do.

Performance Evaluation of Waterflood Reservoirs in High Water Cut Period Based on New Relative Permeability Model

Water flooding performance evaluation and recovery prediction with relatively easy tools has always been among the top aims of reservoir engineering studies, especially for mature reservoirs in high water-cut period. Relative permeability curves are basic required properties reflecting multi-phase flow characteristic and used together with production history to evaluate reservoir performance. In high water-cut period the relative permeability ratio deviate from the empirical straight-line form, which makes traditional models less effective or erroneous in performance prediction. This paper presents a new relative permeability ratio model which can convert the non-linear characteristic of this problem into linear expression. And a new simple water flooding performance analysis technique is developed based on the new model, which can be used to forecast ultimate recovery factor and the corresponding sweep efficiency. The main advantage of this work is taking into account of high water-cut characteristic with less model parameters compared with other improved models. Synthetic case and field examples demonstrated the advantages of this method in parameter solving and consistency in history matching. The proposed technique in this work can be used as predictive analysis tool in forecasting ultimate recovery and performance evaluation for mature water flooding reservoirs.

Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest

This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data. The NMR logging data have some highly vital privileges over conventional ones. The measured porosity is independent from bearer pore fluid and is effective porosity not total. Moreover, the permeability achieved by exact measurement and calculation considering clay content and pore fluid type. Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir. Therefore, achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege. The input parameters of model were neutron porosity (NPHI), sonic transit time (DT), bulk density (RHOB) and electrical resistivity (RT). The outputs of model were also permeability and porosity values. The structure developed model was build and trained by using train data. Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data. Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir. Moreover, the considerable accuracy of newly ANN-Cuckoo method also demonstrated. This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.

NMR derived capillary pressure and relative permeability curves as an aid in rock typing of carbonate reservoirs

Capillary pressure together with relative permeability curves are key parameters in carbonate reservoirs characterization. They serve as fundamental inputs in petrophysical and reservoir engineering studies such as rock typing, fluid contacts determination and saturation height function generation. In the current study, the NMR-T2 distribution diagrams were inverted to the synthetic capillary pressure and relative permeability curves. The results were compared to the laboratory-derived MICP and Kr curves and a satisfactory agreement was achieved between them. Afterwards, the reservoir rock types were determined from the integration of Conventional Core Analysis(CCAL) results with the NMR-derived Pc and Kr curves. Accordingly, the studied carbonate rocks were classified into four hydraulic flow units. Subsequently, a representative capillary pressure and relative permeability curve was derived for each hydraulic flow unit. The methodology is investigated through a case study of the upper Cretaceous Ilam Formation in the Abadan Plain. The results of this study demonstrated that NMR log contributes to a more precise carbonate reservoirs characterization and reducing uncertainty in reservoir rock typing. Through performing some additional computations and calibration with the core results, worthy information is extractable from NMR log.

Performance of the injection of different gases for enhanced oil recovery in a compositionally grading oil reservoir

Reservoir fluid characterization is one of the most important steps in hydrocarbon reservoir engineering calculations and studies. The reservoir fluid composition is not constant along the entire hydrocarbon column and varies along the vertical and horizontal directions. In most cases, such variations have been observed along the vertical direction. This is known as compositional grading phenomenon and has a strong impact on the calculation of original hydrocarbon in place, reservoir development, and oil recovery factor. In this paper, a simulation study was carried out to investigate the effects of compositional grading on reservoir fluid properties and the injection of various gases such as carbon dioxide (CO2), nitrogen (N2), associated petroleum gas (APG), N2–CO2 mixture, and water-alternating-CO2 injection into different depths of a conventional black oil reservoir in the southwest of Iran in order to detect the best injection depth and achieve the highest oil recovery factor. Due to increase in minimum miscibility pressure (MMP) with depth in compositionally grading reservoirs, MMP variations with depth is one of the main challenges in determining the optimal gas injection depth in such reservoirs. The results showed that N2, APG and N2–CO2 mixture were immiscibly injected into all depths of the reservoir due to their high miscibility pressures. The occurrence of gas override and channelling phenomena during the course of immiscible gas injection significantly reduced oil displacement efficiency. On the other hand, CO2 was miscibly injected to all reservoir layers and revealed a higher efficiency in comparison with the injection of N2, APG and N2–CO2 mixture. In fact, better miscibility development was observed in upper reservoir parts, as compared to the lower parts. Through completing injection wells at upper reservoir parts and then injecting gas into these parts, one can thus further enhance oil recovery and extend production plateau. Moreover, the results confirmed that water-alternating-CO2 injection into all reservoir depths, compared to other gas injection scenarios, was associated with increased macroscopic sweep efficiency as well as enhanced oil recovery factor.

The impact of heterogeneity on waterflood developments in clastic inner shelf reservoirs: an example from the Holland Greensand Member, Rotterdam Field, The Netherlands

Abstract A geological understanding of the way fluids flow through a reservoir is crucial when considering waterflood developments in heterogeneous reservoirs. Recent integrated production geoscience and reservoir engineering studies on the Holland Greensand oil reservoir (Aptian) of the Rotterdam Field in the West Netherlands Basin, are used to illustrate the geological controls on fluid flow through rocks deposited in a clastic inner shelf depositional setting. An integrated understanding of the subsurface has been gained through core, log and production data analysis. Analogous Early Cretaceous Upper Greensand Formation coastal exposures in SW England have been used to gauge the scale of lateral and vertical heterogeneity, and its impact on hydrocarbon and water flow paths. Argillaceous sandstones deposited at the distal margins of a subtidal sand-sheet provide internal reservoir baffles, while fluids are believed to preferentially follow flow paths through associated laterally extensive tempestites. Cemented layers observable in wells are interpreted to be laterally restricted and are not considered as major baffles. These observations have been used to steer static reservoir model construction and subsequent simulation. Results from field testing by production logging tool analysis support the geological concepts developed and the modelling approach used. This work has important implications for defining the approach for future reservoir management and redevelopment in the Rotterdam Field, in particular the well type and configuration and completion strategies post-reservoir flooding. The application of methodologies employed in this study is recommended for similar waterflood developments.

Fractured horizontal well productivity prediction in tight oil reservoirs

Horizontal wells productivity prediction and its influence factors analysis have important meanings in development of tight oil reservoirs. The purpose of a reservoir engineering study is to maintain a stable production rate. Although a scientific development plan can benefit yield stability, it needs to match with adjustments of a well production system. Especially for extra low permeability and tight oil reservoirs, an unsteady well production system may increase difficulties of well productivity prediction. Taking a tight oil reservoir in Daqing oilfield as the research subject, on the basis of an unsteady percolation mechanics theory and the superposition principle, a numerical model is built to calculate and analyze the fractured horizontal well productivity. In order to study horizontal well productivity better, a power law exponential decline model is also used to calculate and predict well productivity. Comparing with the numerical model and the power law model, the results show that the numerical model can be used to analyze productivity influence factors, and both models have a good match with actual production data for wells with the stable production system wells. Considering a well's unsteady production system and a model's practicability, the power law model can also be used to calculate and predict horizontal wells productivity in this research oilfield. The results of this study provide a reference for horizontal well productivity calculations and prediction in similar reservoirs.

Smart Horizontal Wells for Development of Thin-Oil-Rim Reservoirs

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 17753, “Smart-Horizontal-Well Drilling and Completion for Effective Development of Thin-Oil-Rim Reservoirs in Malaysia,” by Keng Seng Chan, Rahim Masoudi, Hooman Karkooti, Ridzuan Shaedin, and Mohamad B. Othman, Petronas, prepared for the 2014 International Petroleum Technology Conference, Kuala Lumpur, 10–12 December. The paper has not been peer reviewed. For thin-oil-rim reservoirs, well placement, well type, well path, and the completion methods must be evaluated with close integration of key reservoir and production-engineering considerations. This involves maximizing reservoir-fluid contact and drainage, optimizing well productivity, and optimizing the well life-cycle production profile along the wellbore. Field-implementation cases in Malaysia have shown that this integrated approach can significantly minimize the well count, enhance the well performance, and improve the ultimate recovery per well in thin-oil-rim reservoirs with varying reservoir complexity and uncertainties. Introduction Development of oil-rim reservoirs in Malaysia has been improved progressively in recent years through a series of reservoir-engineering studies and successful field implementations. Although typical oil-rim reservoirs are characteristically wedged between a gas cap and an underlying aquifer, they can be structurally very complicated, with faults and flow boundaries having varying dips and saddles. Oil-rim reservoirs of various sand thickness can also be stacked and compartmentalized. Aquifer support varies from sector to sector, resulting in uneven pressure depletion and fluid-contact change. Wells are completed with multiple strings to produce separately from various different stacked sands with significantly large pressure difference. Well life can be short producing at high watercut and high gas/oil ratio (GOR) because of wells having partial penetration operated at high pressure drawdown. For such a well-development strategy, idle-well rate increases dramatically. Currently, active wells in some key oil-rim reservoirs number far less than 50%. Field oil recovery remains low even after 25 years of continuous production. Force Balance in Thin-Oil-Rim Reservoirs There are several drive mechanisms dominant in oil-rim reservoirs—namely, gas-cap and aquifer expansion, viscous and gravity balance, and solution-gas-expansion drive. Produced-gas reinjection; controlled pressure drawdown at wells in different reservoir sectors on the basis of varying aquifer strength and sand-layer dip angle; and the placement, spacing, and completion of the wells could all affect the balance of these drives. The art of this balance is to keep the oil rim in place, avoiding dramatic migration of the oil rim by having a good vertical displacement conformance.

Analysis and Evaluation of Oil-water Relative Permeability Curves

Oil-water relative permeability curves are the characteristic curves of evaluating the oil-water infiltrating fluid and also are an important part of reservoir engineering studies. Through a large number of core flooding experiments, based on the establishment of oil-water relative permeability calculation models, the use of the function of the relative permeability curves divided these into the water phase concave, water phase convex and a water phase linear categories. The relationship between different types of relative permeability curves with reservoir characteristics, moisture content, common infiltration points, common permeation range and oil displacement efficiency have been evaluated. Analysis the distribution of the reservoir usable remaining oil and reservoir irreducible oil of different types of relative permeability curves.

Smart-Horizontal-Well Drilling and Completion for Thin-Oil-Rim Reservoirs in Malaysia

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 17753, “Smart Horizontal-Well Drilling and Completion for Effective Development of Thin-Oil-Rim Reservoirs in Malaysia,” by Keng Seng Chan, Rahim Masoudi, Hooman Karkooti, Ridzuan Shaedin, and Mohamad B. Othman, Petronas, prepared for the 2014 International Petroleum Technology Conference, Kuala Lumpur, 10–12 December. The paper has not been peer reviewed. For thin-oil-rim reservoirs, well placement, type and path, and well-completion methods, should be evaluated with close integration of key reservoir- and production-engineering considerations. This process involves maximizing reservoir-fluid contact and drainage, optimizing well productivity, and optimizing the production profile along the wellbore. Field-implementation cases in Malaysia have demonstrated that this integrated approach to horizontal wells can significantly minimize well count, enhance well performance, and improve ultimate recovery per well in thin-oil-rim reservoirs with varying reservoir complexities and uncertainties. Introduction Development of oil-rim reservoirs in Malaysia has improved progressively through a series of reservoir-engineering studies and successful field implementations. Although typical oil-rim reservoirs are characteristically wedged between a gas cap and an underlying aquifer, they can be structurally complicated by faults and flow boundaries, featuring various dips and saddles. Oil-rim reservoirs of various sand thickness can also be stacked and compartmentalized. Aquifer support can vary from one reservoir sector to another, resulting in uneven pressure depletion and fluid-contact change. Fig. 1 shows such a vast oil-rim reservoir in peninsular Malaysia. Early oil-rim-reservoir field-development strategy consisted mainly of drilling a large number of vertical or low-angle wells. Wells are completed with multiple strings in order to produce separately from various different but stacked sands with significantly large pressure differences. Well life can be short, producing at high water cut and high gas/ oil ratio (GOR) because of the wells having partial penetration at high pressure drawdown. In such a well-development strategy, idle-well counts increase dramatically. Currently, active wells in some key oil-rim reservoirs are far less than 50% of the inventory. Field oil recovery remains low even after 25 years of continuous production. Force Balance in Thin-Oil-Rim Reservoirs There are several drive mechanisms dominant in oil-rim reservoirs—namely, gas-cap expansion, drive, viscous withdrawal, and gravity balance, in addition to solution-gas-expansion drive. Producing-gas reinjection; controlled pressure drawdown at wells in different reservoir sectors on the basis of varying aquifer strength and sand-layer dip angle; and the placement, spacing, and completion of the wells could all affect the balance of these drives. The art of this balance is to keep the oil rim in place, avoiding pronounced migration. A subtle balance of forces can keep the oil rim in place for many years with minimum pressure decline and more-even fluid withdrawal across the reservoir (Fig. 2). Even though oil-rim reservoirs could have a huge underlying aquifer, the bottomwater support may not be adequate in some oil-rim-reservoir sectors. Some oil-rim reservoirs in Malaysia have observed a significant pressure decline. Water and gas injection should also be implemented, if not for the whole field then selectively in some regions, in order to preserve force balance, minimize oil migration, and improve oil recovery. Injection strategies and methods include periphery or fencing, updip water injection, and downdip gas injection.

Plugging Agent Type Selecting Methods for Channeling Reservoir

The breakthrough flow of injected water and polymer caused by heterogeneous formation is a serious problem in many oil fields, and the problem has become an important restraint to oil development. The profile control and water shutoff plan is determined by experienced and subjective human judgments, the blocking agent selection and plugging agent dosage are lack of scientific basis. Based on a reservoir engineering study, this article proposes an internal pressure calculation model and an injection pressure calculation model, the ultimate gradient of breakthrough pressure and plugging agent working viscosity. Combining methods of plugging agent performance evaluation method, we conclude the matching relation between characteristic parameter of thief zone and performance of plugging agent. A method for selecting a plugging agent in the light of thief zone is established. Case study results reveal the practicability and effectiveness of this method.

An Approach for the Estimation of Dynamic Imbibition Capillary Pressure Curves

Capillary pressure is one of the most important parameters for reservoir engineering studies. Although different experimental methods are devised to measure capillary pressure, these methods do not represent the physics of fluid flow, which happens at reservoir conditions. Thus, it is attempted to extract the capillary pressure from spontaneous imbibition data, the common mechanism of oil production in water wet porous media. In this work, a new approach is developed to obtain the imbibition capillary pressure curve by using spontaneous water imbibition data in oil-water-rock systems. Comparison of calculated imbibition capillary pressure curves by the new approach with experimental values shows a good accordance with each other.

Study on Fluid Solid Coupling of Horizontal Wells in Bottom Water Sandstone Reservoir

In the development of bottom water sandstone reservoir, the utilization of horizontal wells is economical and reliable but also can delay the bottom water coning and it has the advantages over the conventional vertical wells .The methods adopted in the past have methodological errors. This paper regards the reservoir flow and wellbore flow of horizontal well as a interactional system which considers the fluid friction, momentum change, the mixed interference of wellbore wall inflows and other complex factors and obtains Laplace space solution by using the Laplace transform to establish the coupled model of wellbore pressure drop calculation. It can provide more advanced means for reservoir engineering studies, well completion and production engineering design of horizontal wells on the conditions of bottom water reservoir.

Prediction under Uncertainty on a Mature Field

Reservoir engineering studies involve a large number of parameters with great uncertainties. To ensure correct future production, a comparison of possible scenarios in managing related uncertainties is needed. Comparisons can be performed with more information than only a single mean case for each scenario. The Bayesian formalism is well tailored to address the key problem of making predictions under uncertainty, especially in mature fields. It enables to define the reservoir uncertainty taking into account static and dynamic data. This posterior uncertainty can then be propagated to compute probabilistic production forecasts for each scenario, while honoring static and dynamic knowledge of the reservoir. But obtaining posterior uncertainty, as well as propagating it on production forecasts, entails a prohibitive number of reservoir simulations.In this paper, we propose an application of several advanced statistical techniques to perform prediction under uncertainty on a mature field using a reasonable number of simulations. The considered mature field is the PUNQS reservoir model which has been previously used in several comparison studies on uncertainty quantification and history-matching. A workflow based on three steps has been applied. First, a screening and a sensitivity analysis were performed to find the most influential parameters. Then, a probabilistic inversion method was used to reduce uncertainty on the parameters by estimating their posterior uncertainty. Finally, probabilistic predictions are computed by propagating the reduced uncertainty of parameters. In the first step of the workflow, two different sensitivity techniques are discussed and compared. One, more qualitative, based on the Morris method and another, more quantitative, based on Sobol’ indices. In the second step, a probabilistic history-matching procedure is applied to reduce the uncertainty. It is based on both a non parametric response surface approach which uses Gaussian process modeling and an adaptive design strategy. In the final step of the workflow, parametric response surfaces are used to approximate the reservoir production forecasts and obtain their probabilistic distribution by propagating the remaining posterior uncertainty of input parameters.