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RESEARCH & DEVELOPMENTIN COOPERATION WITH UNIVERSITIES AND

RESEARCH INSTITUTES

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CALCAREOUS NANOFOSSILS IN WELLS’ GEOSTEERING

• evaluate the calcareous nanofossil content of formation samples, for possible geosteering.

• Provides a more accurate reservoir layer identification, compared to other methods, e.g. macro fossil biosteering.

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BIO TREATMENT OF HEAVY CRUDE OILUSING MICROORGANISMS AT SURFACE CONDITIONS

• Application of biological treatments to reduce the viscosity and density of unconventional crude oils, as a cheaper and environmentally friendly alternative technology.

• Use of microorganisms isolated from the oil reservoir, with the ability of producing surface active compounds and degrading heavy oil fractions; the use of fungi with the ability of degrading polycyclic aromatic compound; and the use of purified enzymesinvolved in the degradation of polycyclic aromatic compounds.

• So far no clear positive results achieved. Further work needs to be done.

Bacteria Treatment Fungi Treatment Enzymes Treatment

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• Tackling Wax Deposition in paraffinic crude oils in well completions that leads to:

– a drastic reduction in the system performance

– loss of pumping systems efficiency (loss of production).

– Cost efficiency by preventive maintenance

• Goals and Applications:

– Studying the effect of geothermal gradient as the trigger on wax deposition;

– Studying the effect of daily surface temperature variations on wax deposition;

– Analyzing the chemical properties of crude components by developing numerical and experimental investigations in order to understand the crude rheological properties;

– Controlling wax formation and deposition mechanisms

– Estimating the minimal required frequency to perform the wax removal operations in order to guarantee the production string operational conditions

FLOW ASSURANCE IN WELL COMPLETIONS

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Wellhead

PCP pump rotor

Production string

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DEVELOPMENT OF A BIOPOLYMER TO PROMOTE ADDITIONAL OIL RECOVERY

Production of Biopolymers by specific microorganisms

Selection of the microorganism Arthrobacter viscosus

Oil recovery by biopolymer produced by Arthrobacter viscosus

Ad

dit

ion

al

Oil

Re

cove

ry

Commercial vs Biopolymer comparison(AOR of 26%)

Oil Vis: 63.2cP

Future Work Targets:

• Utilization of biopolymer and biosurfactantsimultaneously to promote the EOR

• Production in situ of biopolymer by geneticengineering at reservoir conditions

• Reduction of biopolymer production costs

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• Indigenous microorganisms from thereservoir

• Injection of nutrients to stimulate growth• Lab tests with rock sample at Reservoir

condition• Evaluation of oil recovery improvement

MICROBIAL ENHANCED OIL RECOVERY AT RESERVOIR CONDITIONS

Consortium of microorganisms

Biosurfactant production

Degradation of heavy oil fractions

Enhanced Oil Recovery

↓ interfacial

tension

↓ oil viscosity

↑ oil mobility

Oil Recovery

Additional Oil Recovery 15-17%Incubation w/ Microorganisms

Saturation w/Oil Water flooding

SPE 154598 Biosurfactant Producing Microorganisms and its Application to Enhanced Oil Recovery at Lab Scale

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CO2 SEQUESTRATION WITH GLYMES

• Polyethers has high potential as physical solventsfor CO2 sequestration;

• The project is aimed at the development of athermodynamic tool able to provide an accuratedescription of their thermodynamic behavior;

• Could be further applied for the development andoptimization of industrial processes based onpolyethers.

Elsevier Journal, “New measurement s and modeling of high pressure thermodynamic properties of glycols”, 2017

SPE-188464-MS, “soft-SAFT Equation of State as a Valuable Tool for the Design of new CO2 Capture Technologies”, 2017

I&EC Research-ACS Publications, “New Experimental Data and Mod ling of Glymes: Toward the Development of a Predictive Model for Polyethers”, 2017

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ACOUSTIC SEISMIC INVERSION

Global Seismic Inversion - GSITM

Prior Modelsimulation of AI based on well data (DSS) 1. n AI models

From the N realizations, retain the sectors with best matches and “compose” a best image

of AI

4.

Composed AI model

Next iteration

Conditioned to well + composed AI + CC cube

(coDSS)

5.

FINAL AI model

Correlations

SY vs. real seismic3.

Real seismicn CC cubes

Reflections coefficients

2.

n CR cubes

Convolution with a wavelet

SY generation

n SY cubes

- Iterative inverse modelling approach to generate AI volumes

- No geological framework is required

- Obtaining equiprobable models allows uncertainty assessment

- GSITM has shown capability to achieve a good representation of complex reservoir geometry without an a priori interpretation model

- The method is robust enough to overcome areas of poor seismic quality, without imposing artificial correlations

- Petrel Plugin: Partex Algorithm

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ELASTIC SEISMIC INVERSION

Next iteration

Generation of new Density, Ip and Is models 5.

Reflections coefficients calculation

based on Ip and Is simulated values2.

n CR cubes

Shuey's

Synthetic amplitudes obtained

convolving CR with known wavelets3.

n SY cubes

Selection of best images based on an objective function (average correlation measure)

4. Real seismicn CC cubes

Density Vp Vs

Generation/Joint Simulation of Ip and Is models

1.

n Vp/Vs modelsn Density models(based on wells)

n Vp modelsn Vs models

FaciesGlobal Elastic Inversion - GEITM

- GSITM has been extended to the elastic domain

- Simultaneous inversion of Ip and Is with pre-stack data

- Based on a new geostatistical method of joint images transformation with reproduction of multi-distributions

- This method shows good results and succeeds in identifying potential reservoir areas

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SEISMIC RESERVOIR CHARACTERIZATION

• Porosity constraints

• Trends for static modeling

• Assessment of spatial uncertainty

• Structural and stratigraphic modeling

• Depositional architecture description for reservoir optimization

Improve reservoir characterization based on a geological relationship between seismic and well data

Identify and characterize sub-seismic features, with high vertical variability (observed in the wells)

Global Seismic Inversion

Log Data Seismic Data

Acoustic Impedance Model Porosity Model

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GEOSTATISTICAL SEISMIC INVERSION OF 3D GEOMECHANICAL MODELS

Derived Poisson Ratio

Derived Bulk Modulus

1-D Geomechanical models from well-log data

Derive Regional Stress Indexes

3D geomechanical models conditioned

to well-log and seismic data

Dynamic Moduli

Geostatistical Seismic AVO Inversion

Elastic ModelsSeismic

Quantitative description of rock mechanical and elastic properties and in-situ stresses affecting the subsurface(1) Structural model containing surfaces and faults from seismic data(2) Lithology (including mineral fractions and porosity),(3) Elastic and poro-elastic properties (e.g., Young's modulus, Poisson's ratio, bulk modulus, shear modulus, and Biot's coefficient), (4) Rock strength (such as compressive and tensile strength) and failure properties (such as friction angle),(5) In-situ stresses (such as overburden stress and pore pressure).

Applications: Optimizing reservoir frackability & pore pressure prediction

unconfined compressive strength

in-situ stresses

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INTEGRATING SEISMIC AND PRODUCTION DATAINTO GEOSTATISTICAL MODELLING

COMBINE TWO INVERSE PROBLEMS

into a unique workflowInfer petro-elastic models that simultaneously match

existing production and seismic reflection data

Deviation between synthetic and real seismic data

Deviation between simulated and real production data

Best-fit PorosityReal PorosityBest-Fit Ip

Applications• Optimize history matching and field recovery

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INTEGRATION OF CELLULAR AUTOMATA INTO GEOSTATISTICAL SEISMIC INVERSION

Modeling sedimentary complex structures usingProbabilistic Cellular Automata to reproduce thesedimentary process of deposition of a turbidite system andgeneration of a geologically models for better prediction ofspatial facies distribution.

Characterize the 3D sedimentary architecture ofturbidite reservoirs, represented by thin multi-events(stacked events) using a process-based modeling approach(e.g.) and predict heterogeneities within the reservoir,such as facies distribution and associated uncertainty.

Method - Probabilistic Cellular Automata Objective

Applications• Reservoir compartmentalization• Reservoir sweet spotting

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Objectives

• Integration of local structural and spatial continuity prior knowledge from seismic reflection data into GSI workflow;

• Stochastic simulation of elastic properties using Direct Sequential Simulation with Local Anisotropies (DSS-LA) as perturbation method, contrasting with traditional DSS.

STRATIGRAPHIC GEOSTATISTICAL SEISMIC INVERSION

Proposed Workflow

Comparison between best-fit inverse models from last iteration using DSS and DSS-LA simulation methods

Applications• Improving reservoir recovery• Tackling reservoir compartmentalization

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Objectives

• Characterize the controls for emplacement of igneous features on the Estremadura Spur, its implications for the evolution of the West Iberian Margin and its associated petroleum systems

• Identifying and characterizing sill/dyke emplacement

• Structural response and implications of intruding magmatic units to reservoir compartmentalization and preservation

• Characterization of buried intrusive bodies using geophysical potential methods

Project Framework

• 3 MSc students/research topics

• Faculdade de Ciências, Universidade Lisboa

– Geology

– Geographic Engineering, Geophysics and Energy

Applications

• De-risking exploration activities in Atlantic-type margins

• Reservoir compartmentalization

MAGMATISM IN PASSIVE MARGINS AND ITS IMPLICATIONS FOR EFFECTIVE PETROLEUM SYSTEMS IN THE ATLANTIC

Pereira and Barreto (2018). AAPG Europe Conference