The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Li, Chen (Chengdu University of Technology, China) | Yan, Bicheng (King Abdullah University of Science and Technology, Saudi Arabia) | Kou, Rui (Texas A&M University, United States) | Gao, Shunhua (Texas A&M University, United States)
Abstract The Fast Marching Method (FMM) is a highly efficient numerical algorithm frequently used to solve the Eikonal equation to obtain the travel time from the source point to spatial locations, which can generate a geometric description of monotonically advancing front in anisotropic and heterogeneous media. In modeling fluid flow in subsurface heterogeneous porous media, application of the FMM makes the characterization of pressure front propagation quite straightforward using the diffusive time of flight (DTOF) as the Eikonal solution from an asymptotic approximation to the diffusivity equation. For the infinite-acting flow that occurs in smoothly varying heterogeneous media, travel time of pressure front from the active production or injection well to the observation well can be directly estimated from the DTOF using the concept of radius of investigation (ROI). Based on the ROI definition, the travel time to a given location in space can be determined from the maximum magnitude of partial derivative of pressure to time. Treating travel time computed at the observation well as the objective function, we propose a FMM based deep learning (DL) framework, namely the Inversion Neural Network (INN), to inversely estimate heterogeneous reservoir permeability fields through training the deep neural network (DNN) with the travel time data directly generated from the FMM. A convolutional neural network (CNN) is adopted to establish the mapping between the heterogeneous permeability field and the sparse observational data. Because of the quasi-linear relationship between the travel time and reservoir properties, CNN inspired by FMM is able to provide a rapid inverse estimate of heterogeneous reservoir properties that show sufficient accuracy compared to the true reference model with a limited number of observation wells. Inverse modeling results of the permeability fields are validated by the asymptotic pressure approximation through history matching of the reservoir models with the multi-well pressure transient data.
Jain, Nitesh Kumar (Oil and Natural Gas Corporation Ltd, India) | Chauhan, Parth (Oil and Natural Gas Corporation Ltd, India) | Dadlani, Hitisha Vinodbhai (Oil and Natural Gas Corporation Ltd, India) | Bathla, Priyanka (Oil and Natural Gas Corporation Ltd, India) | Jain, Palash (Oil and Natural Gas Corporation Ltd, India) | Tiwlekar, Sanket Naresh (Oil and Natural Gas Corporation Ltd, India) | Ao, C Moatoshi (Oil and Natural Gas Corporation Ltd, India) | Parmar, Himmatbhai Shambhubhai (Oil and Natural Gas Corporation Ltd, India) | Raturi, Vinay Chand (Oil and Natural Gas Corporation Ltd, India) | Dayal, Har Sharad (Oil and Natural Gas Corporation Ltd, India)
Abstract K Sand of Field N is highly permeable and has variation in viscosity ranging from 5cP to 120cP. K sand has been producing since 1969 and has recovered about 34%. It produces with strong edge water drive mechanism. The primary recovery is about 20-22% where viscosity is 50 to 120cP, produces oil with 90% Water cut. The early rise in water cut was because of water fingers due to adverse mobility ratio. The poor areal sweep has affected the primary recovery which necessitated the application of suitable EOR. The Polymer EOR scheme was conceptualised to address adverse mobility ratio and enhance the sweep efficiency to maximize oil production. The project area was selected for polymer flood considering the moderate primary recovery due to poor areal sweep, structurally up dip area to avoid polymer dilution through aquifer, sand continuity and good reservoir facies. In the first phase, 8 inverted five spot polymer patterns were commissioned. Injection rate is 50 m/d/well and polymer viscosity is 27cP considering the mobility ratio and high dilution effects. The paper explains the efficient planning, monitoring activities, innovative approach to execute the scheme and mid-course correction to improve the performance of the project. The optimised project has resulted in multi-fold increase in production in challenging reservoir conditions. The project is being monitored using pattern balancing based on the production performance monitoring, polymer concentration mapping, historical exploitation, revival of vintage wells, recompletion, and perforation optimization and Injection fluid quality control. The down dip (towards aquifer) loss of polymer is controlled by monitoring liquid rates and polymer presence in the offset wells. Also, to assist the water-cut reduction in updip wells, higher drawdown strategy is implemented for polymer-bank movement in the structural highs. Monitoring of injection fluid through down hole sampling is used to ensure desired quality of polymer. Efficient monitoring of the project has resulted in production enhancement from 100tpd to 260tpd with peak production of 300tpd from targeted area. The innovative approach is to execute the project on fast track through hired injection services. This approach has helped in early realisation of oil and generation of data for preparation of expansion of polymer scheme.
Alan, Cihan (Istanbul Technical University) | Cinar, Murat (Istanbul Technical University) | Onur, Mustafa (University of Tulsa)
Abstract The objective of this paper is to investigate the estimation of layer permeability, skin, and inflow profile from observations of production-logging-tool (PLT) and/or distributed temperature sensing (DTS) for a multilayered system where the layers communicate only through the wellbore. To achieve this objective, we develop a thermal, transient coupled reservoir/wellbore simulator that numerically solves transient mass, momentum, and energy conservation equations simultaneously for both reservoir and wellbore. The simulator accounts for the Joule-Thomson (J-T), adiabatic expansion, conduction, and convection effects for predicting the flow profiles across the wellbore. A comparison of the developed model with a commercial simulator is provided for the single-phase fluid flow of oil or geothermal brine from partially penetrating vertical or inclined wells with distinct fluid and formation properties. A sensitivity study on transient pressure, rate, and temperature profiles to identify the effect of the layer petrophysical properties and the layer thermophysical parameters is also conducted through synthetically generated test data sets from the developed simulator. In addition, nonlinear parameter estimation with the use of both profiles is shown to be useful to reveal permeability and skin information about individual layers. The results show that temperature transient data are more reflective of the properties of the near wellbore region, while wellbore pressures are determined more by average reservoir parameters. The simulator proves practical for designing a PLT test provided that limitations such as single-phase fluid flow having vertical or inclined well equipped with a thorough fluid characterization (EOS) are met. Such design tests may provide a good source for cross-checking PLT flow profiles and validating the fluid contributions from layers that are open to flow. It is often that the spinner of the field PLT tool does not operate properly at very low flow rates. Also, the spinner may fail to calculate and construct PLT plots accurately at very high flow rates. To the best of our knowledge, this is the first study that presents a coupled transient reservoir/wellbore model for predicting layer permeability, skin, and inflow profile of a well from observations of pressure, temperate, and/or rate data from production-logging-tools (PLTs) and/or distributed temperature sensing (DTS) fiber optic cables.
Khan, Mohammad Rasheed (SLB) | Kalam, Shams (King Fahd University of Petroleum & Minerals) | Asad, Abdul (SPRINT Oil & Gas services) | A. Abu-khamsin, Sidqi (King Fahd University of Petroleum & Minerals)
Abstract Unconventional reservoirs like shale oil/gas are expected to play a major role in many unexplored regions, globally. Shale resource evaluation involves the estimation of Total Organic Carbon (TOC) which correlates to the prospective capability of generating and containing hydrocarbons. Direct measurement of TOC through geochemical analysis is often not feasible, and hence researchers have focused on indirect methods to estimate TOC using analytical and statistical techniques. Accordingly, this work proposes the application of artificial intelligence (AI) techniques to leverage routinely available well logs for the prediction of TOC. Multiple algorithms are developed and compared to rank the most optimum solution based on efficiency analysis. Support Vector Regression (SVR), Random Forest (RF), and XGBoost algorithms are utilized to analyze the well-log data and develop intelligent models for shale TOC. A process-based approach is followed starting with systematic data analysis, which includes the selection of the most relevant input parameters, data cleaning, filtering, and data-dressing, to ensure optimized inputs into the AI models. The data utilized in this work is from major shale basins in Asia and North America. The AI models are then used to develop TOC predictor as a function of fundamental open-hole logs including sonic, gamma-ray, resistivity, and density. Furthermore, to strengthen AI input-output correlation mapping, a k-fold cross-validation methodology integrating with the exhaustive-grid search approach is adopted. This ensures the optimized hyperparameters of the intelligent algorithms developed in this work are selected. Finally, developed models are compared to geochemically derived TOC using a comprehensive error analysis schema. The proposed models are teted for veracity by applying them on blind dataset. An error metrics schema composed of root-mean-squared-error, and coefficient of determination, is developed. This analysis ranks the respective AI models based on the highest performance efficiency and lowest prediction error. Consequently, it is concluded that the XGBoost and SVR-based TOC predictions are inaccurate yielding high deviations from the actual measured values in predictive mode. On the other hand, Random Forest TOC predictor optimized using k-fold validation produces high R values of more than 0.85 and reasonably low errors when compared to true values. The RF method overpowers other models by mapping complex non-linear interactions between TOC and various well logs.
Langanke, Nils (Clausthal University of Technology) | Leblanc, Thierry (SNF SA) | Fadili, Ali (Shell Global Solutions International B.V.) | Hincapie, Rafael E. (Clausthal University of Technology) | Ganzer, Leonhard (Clausthal University of Technology)
Abstract The properties of polymeric materials are commonly modified by adjusting the dispersity of the molecular weight distribution, since polymer properties are dominated by intermolecular interactions. We utilized this approach to alter the rheological behavior of polymer solutions for application sub-surface and other porous media flow. We correlate the molecular weight distributions with screen factor measurements and in-situ rheological behavior. Aqueous solutions were prepared using mixtures of partially hydrolyzed polyacrylamide (HPAM) having different molecular weights. The behaviour of the solutions was studied in single-phase flooding experiments using Bentheimer and Berea outcrops, as well as a glass-silicon-glass microfluidic device that mimics porous media. The in-situ rheological behavior determined from flooding experiments was monitored by differential pressure measurements. To improve data accuracy, the core flooding experimental set-up was equipped with multiple pressure sensors along the core. Polymer solutions of same shear viscosity but significantly different dispersities were utilized for the investigation. Elongational viscosities were determined by screen factor measurements. We show that the apparent viscosity during polymer injection is significantly altered for polymer solutions of same average molecular weight but different dispersity. Namely, the onset of shear thickening occurs at lower equivalent shear rates when dispersity is high. Furthermore, the flow of polymer solutions in porous media was correlated to screen factor measurements. This effect of the dispersity of the molecular weight distribution can be used to optimize polymer solution applications in porous materials.
Abstract An innovative optimization methodology for field development planning is presented. A new mixed integer optimizer is described. The optimization tool's "user-friendly" plug-in in a commercial reservoir characterization and simulation package is developed, and methodology applications in exploration projects are outlined. An effective methodology is developed to optimize well placement and facility options in oil fields with multiple reservoirs. The optimized field development plan is selected for individual reservoirs from various well placements, well trajectories, injection strategies, and facility scenarios significantly impacting field oil recovery. Multiple subsurface models representing uncertainties in subsurface descriptions are applied in the optimization process. An effective mixed integer optimizer is developed. The optimizer is based on sequential cycles of a) selection of "promising" scenarios changing one decision variable per simulation and b) evaluations of combinations of the "promising" scenarios using Latin Hypercube sampling. The optimization workflow is implemented as a user-friendly plug-in to a commercial package, which allows one to a) define locations and trajectories of potential wells, b) define well placement and facility scenarios, c) run optimization workflows, and d) evaluate optimization results. The developed optimization methodology is successfully applied in several exploration projects. Effectiveness and significant benefits from the optimization applications are demonstrated. This paper can bring significant benefits to the state of knowledge in the petroleum industry by a) describing the novel methodology for optimizing field development scenarios that have significant impacts on oil recovery, b) applying the new optimizer, c) implementing the optimization plug-in in a commercial package.
Abstract If hydrogen is stored in depleted gas fields, the remaining hydrocarbon gas can be used as cushion gas. The composition of the back-produced gas depends on the magnitude of mixing between the hydrocarbon gas and the hydrogen injected. One important parameter that contributes to this process of mixing is molecular diffusion. Although diffusion models are incorporated in latest commercial reservoir simulators, effective diffusion coefficients for specific rock types, pressures, temperatures, and gas compositions are not available in literature. Thus, laboratory measurements were performed to improve storage performance predictions for an Underground Hydrogen Storage (UHS) project in Austria. A high-pressure-high-temperature experimental setup was developed that enables measurements of effective multicomponent gas diffusion coefficients. Gas concentrations are detected using infrared light spectroscopy, which eliminates the necessity of gas sampling. To test the accuracy of the apparatus, binary diffusion coefficients were determined using different gases and at multiple pressures and temperatures. Effective diffusion coefficients were then determined for different rock types. Experiments were performed multiple times for quality control and to test reproducibility. The measured binary diffusion coefficients without porous media show a very good agreement with the published literature data and available correlations based on the kinetic gas theory (Chapman-Enskog, Fuller-Schettler-Giddings). Measurements of effective diffusion coefficients were performed for three different rock types that represent various facies in a UHS project in Austria. A correlation between static rock properties and effective diffusion coefficients was established and used as input to improve the numerical model of the UHS. This input is crucial for the simulation of back-produced gas composition and properties which are essential parameters for storage economics. In addition, the results show the impact of pressure on effective diffusion coefficients which impacts UHS performance
Abstract Dual energy gamma ray attenuation in multiphase flow meters (MPFMs) are commonly used in the oil and gas industry to measure the flow rate of hydrocarbon producing wells. The low energy gamma ray attenuation is highly dependent on water salinity, making this type of MPFM very sensitive to water salinity. This challenge is evident in oil fields where lower salinity water is injected into the reservoir to maintain pressure for oil producing wells, imposing changes in water salinity with time. Hence, frequent fluid sampling and calibration is required to ensure high reliability measurements from such meters. In this paper, a new methodology is proposed to automatically correct the deviation in MPFM water cut measurements due to water salinity changes without the need to collect fluid samples. The methodology proposed in this paper is a gas-oil ratio (GOR) based method that corrects the deviation in MPFM measurements due to the change in water salinity without the need to collect any fluid sample. In addition, the method does not require any additional information to be acquired from any other sources, other than the normal parameters acquired by the MPFM. The method is applicable to oil wells producing from undersaturated reservoirs. Furthermore, for the method to be reliable, the operating condition at the MPFM has to have adequate amount of free gas because the methodology is GOR based methodology. The results from the proposed method were cross checked against data collected from multiphase separator tests and showed excellent agreement. It was also tested across multiple undersaturated oil reservoirs against a wide range of oil properties and water salinities. The method will help identify the MPFM that require calibration and will eventually make MPFM measurements more reliable without the need for frequent fluid sampling for meter calibration. This method will also lead to reducing the need for rigless multiphase separator testing requirements resulting in substantial cost reduction.
Hosseinzadehsadati, Seyedbehzad (Technical University of Denmark) | Amour, Frédéric (Technical University of Denmark) | Hajiabadi, Mohammad Reza (Technical University of Denmark) | M. Nick, Hamidreza (Technical University of Denmark)
Abstract CO2 injection in depleted oil and gas reservoirs has become increasingly important as a means of mitigating greenhouse gas emissions. This study investigates coupled multiphysics simulations of CO2 injection in chalk reservoirs to better understand the complex thermo-hydro-mechanical-chemical (THMC) processes involved. Two compositional models are created: an isothermal model and a non-isothermal model. Since temperature impacts on fluid compositions have introduced errors in estimating the reservoir's compositions, we made certain simplifications on fluid compositions for the thermal model to address this issue. By using the simplified model, we simulate the temperature propagation of cold fluid into a hot reservoir to observe induced thermal stress due to temperature changes. Despite these simplifications for geomechanical modeling, the propagation of CO2 in the depleted gas reservoir was calculated without considering thermal effects, assuming that the density and viscosity of CO2 remained constant with temperature change in the coupled simulation. Our findings provide valuable insights into the THMC processes involved in CO2 injection in the depleted gas reservoir and highlight the importance of accurately modeling thermal effects to improve simulation accuracy.