Morten Kristensen

Schlumberger (Norway)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Oliver C. Mullins

Schlumberger (United States)

Nikita Chugunov

Sienna Technologies (United States)

Shu Pan

University of Shanghai for Science and Technology

Zulfiquar Reza

University of Oklahoma

Soraya S. Betancourt

State Preservation Board

Carlos Torres‐Verdín

The University of Texas at Austin

Kang Wang

Shanxi University

Tarek S. Mohamed

The University of Texas at Austin

Tom Messonnier

Josselin Kherroubi

Schlumberger (British Virgin Islands)

Cooperative Institutions

Schlumberger (British Virgin Islands)

Schlumberger (United States)

Schlumberger (Norway)

The University of Texas at Austin

Chevron (United States)

University of Oklahoma

Pacific Northwest National Laboratory

Battelle

Schlumberger (France)

Bureau of Economic Analysis

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Deep-Learning-Based Automated Sedimentary Geometry Characterization From Borehole Images

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description (2021)

Marie‐Paule Lefranc Zikri Bayraktar Morten Kristensen Hedi Driss I. Le Nir

Sedimentary geometry on borehole images usually summarizes the arrangement of bed boundaries, erosive surfaces, crossbedding, sedimentary dip, and/or deformed beds. The interpretation, very often manual, requires a good level of expertise, is time consuming, can suffer from user bias, and becomes very challenging when dealing with highly deviated wells. Bedform geometry interpretation from crossbed data is rarely completed from a borehole image. The purpose of this study is to develop an automated method to interpret sedimentary structures, including the bedform geometry resulting from the change in flow direction from borehole images. Automation is achieved in this unique interpretation methodology using deep learning (DL). The first task comprised the creation of a training data set of 2D borehole images. This library of images was then used to train deep neural network models. Testing different architectures of convolutional neural networks (CNN) showed the ResNet architecture to give the best performance for the classification of the different sedimentary structures. The validation accuracy was very high, in the range of 93 to 96%. To test the developed method, additional logs of synthetic data were created as sequences of different sedimentary structures (i.e., classes) associated with different well deviations, with the addition of gaps. The model was able to predict the proper class in these composite logs and highlight the transitions accurately.

Data set

10.30632/pjv62n6-2020a4

Cite

Citations (0)

CO2 geological storage: Critical insights on plume dynamics and storage efficiency during long-term injection and post-injection periods

Journal of Natural Gas Science and Engineering (2020)

Yuliana Zapata Morten Kristensen Nicolas Huerta C. F. Brown C. S. Kabir

Saturation (graph theory)

Petrophysics

10.1016/j.jngse.2020.103542

Cite

Citations (59)

Spectral Response Patterns of Snow and Ice Surfaces for the Landsat Multispectral Scanner (Abstract)

Annals of Glaciology (1987)

Morten Kristensen Norman McIntyre

The high-resolution imagery recorded by systems such as the multi-spectral scanners (MSSs) of the Landsat satellites has revolutionized the study of all types of surface in the polar regions. Visible and near-infra-red imagery has found a wide range of glaciological uses. There is, however, a lack of comparability within and between MSS data which may be a contributary factor to some current problems in interpretation of remotely sensed glaciological data. With the expected continuity of MSS coverage for the forseeable future, it is highly desirable to extend use of the data beyond the basic mapping and feature identification which has made it such a valuable resource. One of the most obvious developments is to investigate characteristics of the reflecting surfaces and to achieve absolute identification of snow and ice surfaces. Although conversion of digital MSS grey tones to radiances enables direct comparison with other sources, automatic identification requires detailed and extensive knowledge of the spectral and reflecting characteristics of surfaces which are to be monitored. This is often best achieved through ground-based observation. In order to provide a base line against which corrected radiances from Landsat MSS data can be compared, a spectrally gated photometer has been used to measure albedo at MSS wave bands in a wide range of conditions. The surfaces monitored in several parts of Norway include sea ice, lake ice, snow, firn and glacier ice, permafrost, and reference surfaces. A range of supporting measurements (including grain-size, surface irregularity, density, level, and free-water content) allows accurate characterization of each surface. This enables identification of spectral-response patterns for each surface category and hence the classification of their reflectances as recorded by the MSS. Examples are given of the application of such classifications to imagery of the polar regions.

Multispectral Scanner

Firn

10.1017/s0260305500000902

Cite

Citations (0)

Compositional Mixing Characteristics of Separate Gas and Oil Charges into Oil Field Reservoirs

Energy & Fuels (2023)

Oliver C. Mullins Hani Elshahawi Li Chen Julia C. Forsythe Soraya S. Betancourt

Many reservoirs worldwide received separate charges of primary biogenic gas and oil. In high pressure provinces such as the Gulf of Mexico, the added gas should be in solution if thoroughly mixed. In addition, this biogenic gas is easily differentiated from thermogenic gas by measurement of methane carbon isotopes and gas composition. Consequently, the extent of mixing can be determined with accuracy, and the gas and oil mixing processes depend on specifics of reservoir charging and can lead to multiple outcomes. The conventional expectation is that the biogenic gas always precedes oil entry into the reservoir. Here, chemical evaluation of reservoir fluids coupled with reservoir understanding of several reservoirs establishes that the oil can arrive prior to biogenic gas. Seismic imaging of gas chimneys provides a plausible explanation for how this can occur. In addition, thermodynamic and geochemical evaluation of the reservoir fluids across the reservoir tightly constrains possible mixing processes. Simple reservoir simulation shows that excellent gas and oil mixing can occur over a wide range of conditions provided that the gas charges into the oil column at its bottom (at the oil–water contact). This modeling exhibits simple fluid mechanics expectations for this reservoir charging process. In addition, since the increase of solution gas destabilizes asphaltenes, fluid geodynamic processes are indicated that can lead to redistributions of asphaltenes. Evaluation of gas and oil mixing processes in reservoirs fits within the framework of the powerful new technical discipline, reservoir fluid geodynamics, for subsurface characterization.

Asphaltene

Associated petroleum gas

10.1021/acs.energyfuels.3c00796

Cite

Citations (11)

Introduction to special section: Wastewater disposal and CO₂ transport in the subsurface

Interpretation (2019)

Christopher F. Brown Ali Fadili Yevhen ‘Eugene’ Holubnyak Morten Kristensen Hannes E. Leetaru

Section (typography)

10.1190/int-2019-0923-spseintro.1

Cite

Citations (0)

Real-Time Formation Evaluation and Contamination Prediction Through Inversion of Downhole Fluid-Sampling Measurements

SPE Reservoir Evaluation & Engineering (2018)

Morten Kristensen Nikita Chugunov Adriaan Gisolf Mario Biagi Francois Dubost

Summary Acquisition of fluid samples using wireline-formation testers (WFTs) is an integral part of reservoir evaluation and fluid characterization. Recent developments in formation-tester hardware have enabled wireline-based fluid sampling in a wide range of downhole conditions. However, accurate quantification of oil-based-mud (OBM) filtrate contamination using data from downhole-fluid-analysis (DFA) sensors alone remains challenging, especially in difficult sampling environments and for advanced sampling tools that have complex inflow geometries and active guarding of filtrate flows. Such tools and conditions lead to contamination behaviors that do not follow simple power-law models that are commonly assumed in OBM-contamination-monitoring (OCM) algorithms. In this paper, we introduce a new OCM algorithm derived from an inversion of DFA data using a full 3D numerical flow model of the contamination-cleanup process. Using formation and fluid properties and operational tool settings, the model predicts the evolution of filtrate contamination as a function of time and pumped volume, and can thus be used to forward model the DFA sensor responses. Sensor data are then inverted in real time to provide contamination predictions. Real-time computation is enabled through fast, high-fidelity proxy models for the cleanup process. The proxy models are trained on and thoroughly vetted against a large number of full-scale numerical simulations. Compared with existing algorithms, the new OCM method is now applicable for all types of sampling hardware and a wider set of operating conditions. By directly relying on a model of the cleanup process, the physical properties of the formation and fluids (such as porosity, permeability, viscosity, and depth of filtrate invasion) are estimated during the inversion, thus providing additional valuable information for formation evaluation. The new method is demonstrated by practical application in both synthetic and field examples of oil sampling in OBM. The synthetic examples demonstrate the robustness of the algorithm and show that the true formation and fluid properties can be recovered from noise-corrupted sensor data. The field example presented demonstrates that contamination predictions are in good agreement with results from laboratory analysis, and the inverted formation properties are consistent with estimates derived from openhole logs and pressure measurements.

Wireline

Inflow

High fidelity

10.2118/187432-pa

Cite

Citations (11)

Measurement and Modeling of Viscous Oil and Tar Mat Formation with a Single, Low-maturity Charge

Energy & Fuels (2024)

Oliver C. Mullins Tarek S. Mohamed Morten Kristensen Shu Pan Kang Wang

Tar mats are common features in carbonate and sandstone petroleum reservoirs in many basins throughout the world and greatly reduce fluid flow through affected rock. They are comprised in part by a solid or very viscous carbonaceous organic phase deposited in the rock pore spaces and in fractures and frequently found at the bottom of the reservoirs at the oil–water contact (OWC). Tar mats can be thin or quite thick (e.g., 10 m thick) and can be patchy or laterally continuous across the entire OWC of a reservoir. Tar mats and possible associated viscous oil have a huge impact on pressure support and aquifer sweep during oil production. In spite of their importance, two key properties of tar mats have not been explained in the literature: (1) the mechanism of their formation and (2) why tar mats are at the base of the reservoir. In this article, these two questions are resolved; the predominant mechanisms of formation of a tar mat and viscous oil column is clarified for a large reservoir representative of a simple class of reservoirs; those with a single low maturity charge, and with no alteration processes such as biodegradation. Here, extensive chemical analysis of the tar and viscous oil is reviewed constraining possible explanations for the origin of the tar mat. The formation of the tar mat and viscous oil is demonstrated numerically using reservoir flow simulation (Eclipse) using 2D model simulations. First-principles fluid mechanics considerations support simulation models. The nanocolloidal characterization of asphaltenes in oil codified by the Yen–Mullins model is key to predicting viscous oil and tar mat distributions at the 100 km length scale. Boycott convection is responsible for transport of asphaltene gravity currents across reservoir length scales. In addition, the role of Boycott convection in inhibiting tar mat formation clarifies why tar mats are generally form only after the reservoir charge is complete, and thus found at or near the OWC. With the understanding and modeling of this "simple" process of viscous oil and tar mat formation, more complex processes such as those involving multiple, incompatible charges are now readily accessible to reservoir simulation and for forecasting production especially with water injection.

tar (computing)

10.1021/acs.energyfuels.4c04406

Cite

Citations (0)

Analysis of Lateral Fluid Gradients From DFA Measurements and Simulation of Reservoir Fluid Mixing Processes Over Geologic Time

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description (2021)

Qing Chen Morten Kristensen Yngve Bolstad Johansen Vladislav Achourov Soraya S. Betancourt

Downhole fluid analysis (DFA) is one pillar of reservoir fluid geodynamics (RFG). DFA measurements provide both vertical and lateral fluid gradient data. These gradients, especially the asphaltene gradient derived from accurate optical density (OD) measurements, are critical in thermodynamic analysis to assess equilibration level and identify RFG processes. Recently, an RFG study was conducted using DFA and laboratory data from an oil field in the Norwegian North Sea. Fluid OD gradients show equilibrated asphaltenes in most of the reservoir, with a lateral variation of 20%. This indicates connectivity, which is confirmed by three years of production data. Two outliers are off the asphaltene equilibrium curve implying isolated sections, one each on the extreme east and west flank. Their asphaltene fraction varies by a factor of six. Such a difference reveals that different charge fluids entered the reservoir, and the equilibrated asphaltenes are the result of an after-charge mixing process. Meanwhile, different gas-oil contacts (GOCs) exist in the reservoir, indicating a lateral solution-gas gradient. Geochemistry analysis shows the same level of mild biodegradation in all the fluid samples, including those from two isolated sections. This means that biodegraded oil spills into the whole reservoir with little or no in-reservoir biodegradation. Furthermore, lateral asphaltene gradients at different times after charge have been preserved; it was a factor of six in asphaltenes content initially and is now 20% in the present day. This unique data set provides a valuable constraint to simulate reservoir fluid after-charge mixing processes to present day, aiming to investigate the factors impacting the evolution of lateral composition gradients in geologic time in a connected reservoir. Numerical simulations were performed over geologic time in reservoirs filled by oil with a lateral density gradient, which imitates the lateral compositional gradients in the gas-oil ratio (GOR) and asphaltenes measured in the above oil field. Simulations show that this lateral gradient creates lateral differential pressures and causes a countercurrent fluid flow forming a convection cell. In reservoirs with realistic vertical-to-horizontal aspect ratios, such fluid flows are not rapid, and lateral gradients can be partially retained in moderate geologic times. Additionally, diffusion was included in the simulation. The reservoir model was initialized with two GOCs producing subtle lateral GOR and density gradients. The simulated mixing process transports gas from higher GOR regions to lower GOR regions and reduces the GOC difference. However, the flux of solution gas transport is small. Consequently, we conclude that lateral GOR and asphaltene gradients can persist for moderate geologic time, which is consistent with observation from the field.

Asphaltene

10.30632/pjv62n1-2021a1

Cite

Citations (4)

Proxy-Enabled Stochastic Interpretation of Downhole Fluid Sampling Under Immiscible Flow Conditions

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description (2018)

Morten Kristensen Nikita Chugunov Koksal Cig Richard Jackson

Proxy (statistics)

10.30632/pjv59n5-2018a5

Cite

Citations (5)