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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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
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.
Samarkin, Yevgeniy (King Fahd University of Petroleum and Minerals) | Amao, Abduljamiu Olalekan (King Fahd University of Petroleum and Minerals) | Aljawad, Murtada Saleh (King Fahd University of Petroleum and Minerals) | Sølling, Theis Ivan (King Fahd University of Petroleum and Minerals) | AlTammar, Murtadha J. (Saudi Aramco) | Alruwaili, Khalid M. (Saudi Aramco)
Abstract Fractured carbonate formations composed of chalk and limestone rock lithologies develop several issues over time, reducing fractures’ conductivity. One such issue is the embedment of the proppant that happens due to the soft nature of the carbonate rocks. Reduction of fractures’ conductivity results in the need for refracturing operations that require pumping tremendous amounts of water. The refracturing operations can be avoided if the fractures are maintained conductive for a longer time. This research targets reducing the severity of proppant embedment issues in carbonate formations through rock hardening by diammonium hydrogen phosphate (DAP) treatment. The chalk and limestone rock samples were treated with a DAP solution of 0.8M concentration at three temperatures, namely 30°C (ambient), 50°C, and 80°C. The samples were treated by immersion in solution, in which rocks were kept reacting for 72 hours. The treated samples were analyzed using the SEM-EDX technique to identify new minerals and changes in the morphology of the rock samples. Moreover, the changes in the hardness of the samples were analyzed by the impulse hammering technique. In addition, the proppant embedment scenario was mimicked in the rocks by utilizing Brinell hardness measurements before and after their treatment. The SEM analysis demonstrated that the treatment of carbonate rocks with a DAP solution results in the formation of hydroxyapatite (HAP) minerals. In addition, it was observed that the temperature of the treatment affects the crystallization patterns of the HAP minerals. Further results demonstrated that DAP treatment at elevated temperatures significantly improves the hardness of the samples. Young’s modulus of the rock samples increased by up to 60 - 80% after the treatment. In addition, studies have shown the improvement of rocks’ resistance to indentations. The sizes of the dents created by the Brinell hardness device were smaller than before the treatment. Overall, it was demonstrated that the Brinell hardness of the rock samples improved by more than 100%. This research demonstrated that treating carbonate rocks with DAP solution results in their hardening and improved samples’ resistance to indentation. Moreover, the treatment of rock samples at temperatures similar to reservoir conditions even further improves the mechanical properties of the carbonate rocks. Upscaling laboratory DAP treatment techniques for reservoir applications will introduce new practical methods for maintaining the long-term conductivity of propped fractures. Such a procedure will help avoid refracturing operations, resulting in better and more sustainable management of water resources.
Abstract Injecting fluid into subsurface strata has the potential to cause earthquakes by altering pore pressure and subsurface stress. To assess the seismic hazard associated with subsurface flow processes, it is necessary to understand the underlying mechanics of fluid-induced fault reactivation. In this study, we conduct a coupled hydro-mechanical modeling of fluid injection to a strike-slip fault with rate-and-state friction. We account for the fluid flow across and along the fault, as well as the hydromechanical properties of faults in the normal and tangential directions. We model the injection-induced slip of a strike-slip fault, and the simulation results indicate that there are two primary factors that affect injection-induced seismicity. The first factor is that the initiation of rupture is directly related to the diffusion of pore pressure in the near field where there is high shear stress and a large reduction in fault strength due to the significant pressure change. The second factor is that the transfer of shear stress from the nucleation zone promotes the advancement of the slip front to the near- and far field. Our results are quite conservative since the model chose pf as the relevant pressure when calculating the effective normal stress and the shear stress has a slight effect on the pressure variation. Finally, the sensitivity analysis indicates that greater tangential permeability values delay the onset of fault rupture and diminish the likelihood of fault reactivation. Higher stiffness induces fault slip earlier but reduces its magnitude.
Abstract Hydraulic fracturing has long been an established well stimulation technique in the oil & gas industry, unlocking hydrocarbon reserves in tight and unconventional reservoirs. The two types of hydraulic fracturing are proppant fracturing and acid fracturing. Recently, a new of hydraulic fracturing is emerging which is delivering yet more enhanced production/injection results. This paper conducts a critical review of the emerging fracturing techniques using Thermochemical fluids. The main purpose of hydraulic fracturing is to break up the reservoir and create fractures enhancing the fluid flow from the reservoir matrix to the wellbore. This is historically achieved through either proppant fracturing or acid fracturing. In proppant fracturing, the reservoir is fractured through a mixture of water, chemicals and proppant (e.g. sand). The high-pressure water mixture breaks the reservoir, and the proppant particles enter in the fractures to keep it open and allow hydrocarbon flow to the wellbore. As for acid fracturing, the fractures are kept open through etching of the fracture face by acid such as Hydrochloric Acid (HCl). An emerging technique of hydraulic fracturing is through utilization of thermochemical solutions. These environmentally friendly and cost-efficient are not reactive as surface conditions, and only react in the reservoir at designated conditions through reservoir temperature or pH-controlled activation techniques. Upon reaction, the thermochemical solutions undergo an exothermic reaction generating in-situ foam/gases resulting in creating up to 20,000 psi in-situ pressure and temperature of up to 700 degrees Fahrenheit. Other reported advantages from thermochemical fracturing include the condensate bank removal (due to the exothermic reaction temperature) and capillary pressure reduction.
Abstract Survivorship bias is a well-known tendency to overweight available data and underestimate the missing information. Cañadón León in San Jorge basin, Argentina is a waterflooded field with a current water-cut of 95% where innovative recovery strategies such as Chemical Enhanced Oil Recovery (cEOR) become a condition for further development. Data acquisition is often biased towards the best reservoirs, leading to major uncertainty in assessing opportunities in mature fields. After 70 years of primary oil production and water injection, the study aims to evaluate the remaining opportunity, which leads to a double challenge: Estimation of bypassed oil during the inefficient waterflooding process because of poor mobility ratio and the potential of marginal reservoirs. Initial stage field exploitation and data acquisition at early stages of development aimed mainly to characterize the higher oil-saturation zones with better petrophysical properties, leading to a lack of data on marginal reservoirs which become critical targets for mature reservoirs analysis. The data interpretation within a semi regional geological framework to build the static model, allowed a representative construction of poorly characterized reservoirs due to survivorship bias effect. Several hypotheses were evaluated with dynamic simulation to avoid assuming recoverable oil based on survivorship bias due to missing information in secondary targets. Integration of what-if scenarios, both static and dynamic, and assessment of uncertainty provided a better understanding of critical constraints and optimum ranges of variability to analyze cEOR with polymer injection. A wide variety of fluid saturation scenarios, mobility ratios and reservoir properties were considered to quantify the field potential. Sensitivity analysis helped to identify the most relevant uncertainties in history matching and reliability in forecast: Primary gas cap contact and its expansion, water-oil contact, the transition zone (oil-water system), fluid mobility ratios and polymer characteristics. A major benefit from polymer injection is CO2 emissions reduction per barrel of oil by more than 40% compared to water injection, reducing project carbon footprint. Development strategy achieves a short-term incremental recovery factor of 10% with a total of 68 wells in 20 injection patterns (considering a period between 3 to 6 years due to oil production acceleration). This methodology allowed to establish the foundations for development strategies based on multi-modelling within conceptual geological frameworks reflecting the impact of the recognized uncertainties. This technique does not allow to determine the unknowns, but it does allow to estimate their impact.
Abstract Denmark aims at a 70% reduction in greenhouse gas emissions by 2030 compared to levels measured in 1990, with a long-term target of becoming carbon-neutral by 2050. As part of this national effort, the Bifrost project, aims at repurposing two depleted gas fields in the Danish North Sea for CO2 storage, namely the Harald West sandstone field as the primary target and the neighboring Harald East chalk field as a potential upside. The Harald East chalk is the focus of this study. The storage potential and infrastructures available within the multiple chalk fields located in the Danish North Sea represent valuable assets to fulfill the national objectives enabling a time- and cost-efficient implementation of carbon storage activities. One of the main challenges for carbon storage in chalk is the contradictory experimental results reported in literature that indicate both a strengthening and a softening effect of supercritical CO2 on the plastic and elastic properties of chalk. Such uncertainty hampers accurate prediction of the deformation response of storage sites. In this context, the study aims at assessing the impacts of two levels of uncertainty; the type of mechanical alteration induced by supercritical CO2 and the petrophysical heterogeneity on the long-term deformation behaviour of chalk reservoirs. An in-house hydro-mechanical-chemical model calibrated against experimental data on chalk is applied in a reservoir model of the Harald East field. A 16 year-long injection period is simulated assuming two scenarios. In scenario 1, supercritical CO2 has no impact on the mechanical properties of the rock, whereas in scenario 2, a 30% and 25% lowering of the pore collapse stress and elastic modulus of chalk is assumed. A systematic comparison of the flow and mechanical behaviour of low and high porosity cells located in the vicinity of an injection well indicates that the impact of CO2 on the mechanical properties of chalk, the distance of the cells from the injector, the local stress redistribution taking place in the reservoir between mechanically soft and strong cells, and the presence of natural gas in pore space before CO2 injection are key factors controlling the amount and distribution of plastic deformation occurring in the storage site. The outcome of this work enables quantifying the main risks associated with rock compaction close to and further away from injectors during and after carbon storage in chalk fields.
Mariotti, Pamela (Eni S.p.A.) | Toscano, Claudio (Eni S.p.A.) | Vecera, Carmela (Eni S.p.A.) | Da Marinis, Annunziata (Eni S.p.A.) | Frau, Simone (Eni S.p.A.) | Poggio, Franco (Eni S.p.A.) | Pangestu, Imam (Eni Muara Bakau BV) | Praja, Kurna (Eni Muara Bakau BV)
Abstract Currently the oil and gas industry is becoming more digitalized. The abundance of data varieties that are recorded has driven the industry to move forward from the conventional data management to more fashioned data acquisition. The field under study (Field A) is a deep-water gas asset, characterized by a complex internal architecture of many separate and discrete gas charged stacked sand bodies. Objective of this paper is to show the key role of the reservoir monitoring strategy, fully integrated in a multidisciplinary workflow that allowed to detail the reservoir conceptual model leading to the identification of valuable production optimization opportunities. Field A produces through smart wells with selective completions, equipped with permanent down hole gauge (one for each open layer) allowing Real Time Monitoring of the key dynamic parameters (e.g., rate, flowing bottom hole pressure) and implementation of surveillance actions such as selective Pressure Transient Analysis. A workflow is implemented to be able to describe each open layer performance integrating all available data starting from well back allocation verification through virtual metering implementation. Then, Inflow Performance Relationship per layer is used to back-allocate well production to each unit. Robust continuous update of material balance analysis for each layer allowed to verify alignment between the geological gas volume in place and the dynamic connected volume, leading to update coherently also the dynamic model. Comparison between geological gas volume in place and dynamic connected one triggered a revision of geological modelling, reviewing seismic uncertainty and facies modelling, trying to embed dynamic evidence. Among parameters taken in account, layers internal connectivity resulted as the most impacting one. The revised model allowed to identify and rank residual opportunities on developed layers and possible additional explorative targets. The result of this screening led to the strategic business decision to plan an infilling well, with primary target the best unexploited sub-portion identified inside one of the analyzed layers together with other stacked minor targets. The expectation of primary target resulted confirmed by the data acquired in the new well drilled. Moreover, the real time monitoring workflow has been implemented in a digital environment for continuous automated update resulting in continuous reservoir monitoring and management. The successful experience on Field A proved the key role of a structured Reservoir Monitoring strategy as "drive mechanism" for a decision-making process extremely impacting on the core business. The automation of data extraction, will lead the way to an increasingly efficient use of "big amount" of data coming from real time monitoring, thus further improving the overall process of asset maximization opportunities identification.
Abstract Representation learning is a technique for transforming high-dimensional data into lower-dimensional representations that capture meaningful patterns or structures in the data. Uniform manifold approximation and projection (UMAP) enables representation learning that uses a combination of nearest neighbor search and stochastic gradient descent in the low-dimensional graph-based representation to preserve local structure and global distances present in high-dimensional data. We introduce a new technique in representation learning, where high-dimensional data is transformed into a lower-dimensional, graph-based representation using UMAP. Our method, which combines nearest neighbor search and stochastic gradient descent, effectively captures meaningful patterns and structures in the data, preserving local and global distances. In this paper, we demonstrate our expertise by utilizing unsupervised representation learning on accelerometer and hydrophone signals recorded during a fracture propagation experiment at the Sanford Underground Research Facility in South Dakota. Our UMAP-based representation executes a five-step process, including distance formulation, connection probability calculation, and low-dimensional projection using force-directed optimization. Our analysis shows that the short-time Fourier Transform of signals recorded by a single channel of the 3D accelerometer is the best feature extraction technique for representation learning. For the first time, we have successfully identified the distinct fracture planes corresponding to each micro-earthquake location using accelerometer and hydrophone data from an intermediate-scale hydraulic stimulation experiment. Our results from the EGS Collab project show the accuracy of this method in identifying fracture planes and hypocenter locations using signals from both accelerometers and hydrophones. Our findings demonstrate the superiority of UMAP as a powerful tool for understanding the underlying structure of seismic signals in hydraulic fracturing.
Abstract This paper demonstrates how supervised machine learning (ML) aids planning and acquisition of wireline formation testing (WFT) in thin laminated sands. Available well data was used to train a set of algorithms to identify intervals where tests are likely to fail. The trained model aims to prevent WFT failures what in turn results in reduced rig downtime, increased efficiency of the logging contracts and improved reservoir characterization. Wireline formation testing is essential to acquire rock and fluid characteristics in multilayered reservoirs setting up the base for the upcoming decisions. This becomes significantly complicated in thin laminated sands with a thick hydrocarbon column, requiring hundred(s) of points to meet given objectives. The percentage of failed tests can be much higher than those of being successful. In this context an automated advisory system, based on the abundant historic WFT dataset, can mitigate personal biases of the subsurface team and boost the share of successful tests in future wells. A combined set of wireline logs served as features to predict WFT outcome in two classification approaches. Binary classification predicts likelihood of having a good test or a failure, whereas multi-classification further details failure types into 5 categories. The overall dataset comprised more than 500 points (testing attempts) within the concession to train various ML models, using variety of preprocessing and hyper parameters. Their accuracy and area under curve (AUC) were used as the ranking criteria. The performance mostly depends on the number of classes to be predicted, the number of input features and the number of data points available for training. Less classes to predict and more input features result generally in better model metrics. The final selected model attained a maximum accuracy of 0.75 in two exploration wells in the adjacent concessions, i.e. correctly predicting 75 outcomes out of 100 wireline formation tests. A log interpretation suite accesses the deployed model via a cloud endpoint for the upcoming infill wells. The approach could improve wireline formation testing in other reservoirs or regions prone to WFT failures, where accumulated data is sufficient for machine learning applications. This could result in tangible savings during well operations.