Beicip Franlab (France)

Traditionally, in faulted/sedimentary systems, geothermal injection wells are drilled away from known faults to reduce the risk of induced seismicity, e.g., in the VITO geothermal project in Mol, Belgium. Unless there is high layer/matrix permeability in the area, this can limit the well capacity and result in prohibitively high injection pressures that can induce high stress changes on smaller potentially unknown faults in the area. Further, in geothermal applications, temperature changes in the subsurface may induce large stress changes that can also result in fault reactivation. As part of the HEU-URGENT project, a risk management toolbox has been developed to allow for more effective placement of geothermal wells in doublet systems to maximize heat production while minimizing the risk of induced seismicity.An essential part of the risk management toolbox is the geomechanical solution to evaluate effective stress changes on known faults and structures in or near the reservoir. In this work, we evaluate three techniques for stress calculation. The first is a simple, but fast, 1D semi-analytical calculation based on Hooke’s law for elasticity. This approach allows for direct pore pressure and thermal stress effects to be considered but ignores elastic stress transfer and Poisson effects. The second semi-analytical approach utilizes the nucleus of strain concept to account for the elastic stress transfer and Poisson effects but comes at a higher computational cost. The third approach is a numerical finite element approximation of linear elastic material behavior. The pros and cons of each approach will be discussed, and the comparison of the results will be presented.

Abstract Interwell connectivity evaluation is a fundamental component of reservoir management in both mature and undeveloped fields. This is particularly critical in geologically complex settings characterized by faulting, stratigraphic discontinuities, and potential compartmentalization. Pulse testing, which involves generating pressure pulses in a generator well and observing pressure responses in other wells (observation wells), presents a cost-effective method for assessing connectivity because no producers are shut-in (C.R. Johnson; R.A. Greenkorn). However, its practical implementation is often constrained by signal detection challenges-especially in low-permeability formations, reservoirs with high gas saturation, or when wells are widely spaced. In such cases, pressure signals become weak and easily obscured by operational noise or unrelated reservoir dynamics, making conventional interpretation approaches such as transient pressure analysis or numerical simulation less effective. This paper presents a dedicated pulse test workflow developed to overcome these limitations. The workflow integrates advanced signal processing techniques with analytical modelling to ensure both detectability and interpretability of pulse responses in complex and challenging environments. A key part of the workflow, critical for pulse test design and interpretation, is based on signal decomposition, statistical correlation techniques, and analytical models. In the interpretation stage, the method decomposes measured pressure data into signal components attributable to the pulsing well and background noise. By applying diffusion-based mathematical models to isolate and match the response, the workflow quantifies interwell transmissibility and storativity, while also estimating associated uncertainty based on the signal-to-noise ratio. The automated interpretation workflow consists of three main stages: Preprocessing: Removes background trends and noise using detrending, highlighting periodic features in gauge data,Signal Decomposition: Splits pressure data into two parts-one attributed to the pulsing well, the other to unrelated events – allowing a cleaner interpretationParameters Estimation: Calculates interwell transmissibility and storativity, with uncertainty linked to the signal-to-noise ratio and connectivity confidence. The methodology was applied in Field A, a clastic onshore oil reservoir with compartmentalization and an expanding gas cap. The pulse test campaign included three 240-hour cycles involving one injector and three producer-observation wells. The proposed signal decomposition approach enabled the identification of pressure responses with up to 86% correlation to the pulsing sequence, overcoming interference from operational noise and variable reservoir conditions. The derived time lags and amplitudes were used to compute interwell transmissibility, providing a quantified measure of reservoir connectivity across fault blocks. These findings supported decision making related to optimization of the injection strategy and justified new well placement decisions for a planned infill injector. This work demonstrates that automated signal processing significantly enhances the reliability and applicability of pulse test interpretation in reservoirs where the application of traditional methods is challenging. The workflow enables: Interpretation in high gas saturation and compartmentalized reservoirs previously considered unsuitable for pulse testing.Quantitative evaluation of signal presence and strength, offering confidence metrics for interwell connectivity.Operational efficiency, with no need for complex forward modelling or heavy manual curve matching.Scalability, supporting multi-well, multi-cycle pulse test campaigns. Ultimately, this paper provides a methodology and general workflow that extends the utility of pulse testing into more challenging reservoir environments by leveraging signal decomposition and analytical modelling.

The morphology of a coral reef terrace is a key parameter in the interpretation and quantification of past sea-level changes, but it is directly influenced by local morphodynamic and hydrodynamic conditions. Spatial differences in terrace morphology t

Abstract Accurate characterization of capillary pressure and relative permeability curves is crucial for reservoir modelling and production optimization. However, in many cases, laboratory measurements are only available for reservoir rocks of better quality, leaving those of lower quality lacking essential data. This paper proposes a methodology that combines log data and laboratory measurements to assign capillary pressure and relative permeability curves to different reservoir rock types, even when laboratory data is limited. The suggested methodology was successfully applied to sandstone reservoirs in a northern Africa field. First, all cored sample depths are converted to the corresponding log depths, and each sample is assigned to the most probable reservoir rock type (RRT) identified in the petrophysical analysis. Subsequently, Pc values are transformed into height above the free water level (HAFWL) curves, and these undergo a thorough screening process to filter out less confident samples. Higher confidence HAFWL curves are then grouped according to their respective RRTs. The subsequent step involves defining a reference synthetic Sw vs. HAFWL curve for those RRTs with assigned laboratory data. For the RRTs lacking laboratory data synthetic curves are then obtained by using log-identified irreducible water saturation (Swi) values above the transition zone. Finally, this part of the process concludes with the validation of the reference curves by comparing them to Sw profiles obtained from log interpretation. Next, attention shifts to kr data, beginning with curves pre-processing. This entails determining endpoints and Corey coefficients for all supplied curves to construct analytical kr curves. The subsequent step involves an innovative screening technique of kr curves based upon the use of cross-plots between petrophysical parameters and kr curve endpoints. This process serves two primary purposes: firstly, it allows filtering anomalous samples and categorise representative curves per RRT, and secondly, it identifies trends in the cross-plots. The latter will enable the creation of synthetic kr curves for RRTs lacking laboratory data. In summary, the proposed methodology offers a systematic approach to assign Pc and kr curves to reservoir rock types, effectively leveraging available laboratory data while addressing data gaps for less characterized rocks. Emphasis is placed on innovative screening techniques for kr curves. By incorporating synthetic curves and validating against log data, this methodology enhances the accuracy and reliability of Pc and kr curves, thereby supporting improved reservoir characterization and simulation studies for optimized hydrocarbon recovery.

Summary This paper describes a 3D basin modelling study conducted offshore Sarawak Basin, aiming at predicting basin-wide CO2 distribution. Two set of models were constructed: regional basin-scale and block-scale. The regional basin-scale model was able to explain the CO2 concentration and carbon isotopic changes observed in Well-X. The block-scale model, however, failed to replicate the observed CO2 variations. This study underscores the importance of having regional basin-scale models.

Summary Aiming at de-risking exploration by predicting stratigraphy and facies architecture in unexplored/ less explored areas, forward stratigraphic modelling (FSM) reconstructs facies architecture through geological time at basin or appraisal scale while honouring sequence stratigraphy models based on core, well logs and seismic data. Petroleum System Modelling (PSM) dynamically simulates the evolution of a sedimentary basin through geological time, assessing generation, migration, trapping and accumulation processes and thus contributing thoroughly to play assessment. It predicts accumulated volumes in reservoir formations as well as the quality of trapped hydrocarbons through rigorous compositional modelling. Using seismic inversion and Quantitative interpretation (QI) techniques we can transform seismic traces into lithologic properties, which helps to reduce the uncertainty related to reservoir quality. These techniques results have different levels of vertical and spatial resolution and extent, but each provides valuable information about geological properties and petroleum system elements, by integrating the results from these techniques we can reduce exploration risk. This paper describe the integration of FSM, PSM and Seismic QI studies to de-risk the identified leads/ prospects in exploration block located in Offshore Suriname basin.

Abstract This study aimed at history-matching a full-field numerical model representing a mature oil field that underwent polymer flooding for several years. The main technical challenge was to account for the polymer thermal degradation and the change of polymer molecule that occurred during the development of the field. Accurately representing these changes was crucial for satisfactory oil production history matching. Advanced simulation software was used to simulate all relevant mechanisms, resulting in a successful history matching of the production data. An innovative workflow based on an integrated approach to define geobodies and connections between injectors and producers was implemented to obtain a satisfactory model calibration on the initial water flooding phase. To model the subsequent polymer flooding phase, a single polymer model was not sufficient to accurately represent the impact of polymer injection on oil production rate, in particular towards the end of the historical period. The simulation model was therefore improved using advanced EOR capabilities involving multi-polymer simulation, advanced adsorption model and thermal degradation. Adjusting these properties supported by laboratory data yielded a robust history-match on the whole polymer flooding period.

Abstract To meet energy demand while reducing CO2 emissions in a carbon constrained future, one of the key milestones of the roadmap proposed by the International Energy Agency is to stop new oil and gas field developments and extend instead existing fields’ lifetime. Waterflood and EOR recovery methods aiming at optimizing mature fields’ oil production thus appears as technologies of choice. Nonetheless, oil production by waterflood is energy and therefore carbon intensive, especially for oil fields exhibiting high water-cuts, due to produced water handling. Recent communications suggest that chemical EOR processes such as polymer flood reduce the CO2 emitted per barrel of produced oil compared to water flooding, as they reduce water cut and/or accelerate oil production. Few papers however assess the CO2 footprint of surfactant-based processes. In this work, we aim to compare the carbon emissions of different chemical EOR scenarios including polymer, surfactant-polymer and alkaline-surfactant-polymer, with a reference waterflood scenario. We present an exergy-based methodology to estimate energy gains obtained from water-cut decrease while taking into account process efficiency, including oil production, water treatment required for chemical use and chemical production. The corresponding CO2 emission reduction is then estimated from these energy gains. This methodology was applied to two case studies available in the literature, the Mangala field polymer flood pilot and the Taber alkaline-surfactant-polymer flood. Necessary injection and production data were collected to extrapolate production water rate and WOR using Decline Curve Analysis. Results show that implementing polymer or alkaline-surfactant-polymer flooding after waterflooding allows wells to reach higher oil production rates, up to twice as much. In addition, water-oil ratio is more than ten times reduced i.e. less produced water has to be handled while producing more oil. Thus, we show that for both of these fields oil production is less carbon intensive. Furthermore, we considered a hypothetical surfactant-polymer flood to compare to the Taber alkaline-surfactant-polymer flood. Water treatment as well as chemical production energy costs are reduced for the surfactant-polymer due to the absence of added alkali, which induces additional CO2 emission reduction. This study highlights that chemical EOR, in particular polymer and/or surfactant-based processes stands as a potential solution to reduce the carbon footprint of oil recovery while maintaining the production required to sustain the world's energy consumption.

Summary This abstract presents the workflow built in order to forecast well production parameters and its application on the Volve field dataset. It consists of a sequence of four neural networks trained to forecast well downhole pressure, total liquid rate, water-cut and wellhead pressure. This workflow allows the engineers to rapidly evaluate scenarios for their day-to-day optimization operations.

Summary Natural hydrogen systems work somewhat like petroleum systems with sources, migration paths, reservoirs, seals, and traps. Natural hydrogen can be produced from source rocks but above all by other mechanisms such as radiolysis, serpentinization, oxidation of rocks rich in FeII. Once generated, hydrogen can migrate either in the vapor phase or dissolved in water. Dissolved hydrogen can outgas at shallow depths depending on pressure and temperature conditions and accumulate in reservoir rocks. Recent studies provide the physical and chemical properties of hydrogen thus allowing the comparison between the petroleum system and the hydrogen system. The efficiency of the hydrogen system (HSY) must be lower than the efficiency of the petroleum system (PSY). Therefore, the mass balance performed on the global hydrogen system shows some inconsistencies in considering natural hydrogen as a major source of sustainable energy. Considering published hydrogen flux values and hydrogen production values from major recognized sources, the resulting concentration factors (CF) are several orders of magnitude higher than those observed for hydrocarbons. Is this realistic or is there a bias in the interpretation of surface measurements or is there a missing source of natural hydrogen?