NAPENews Magazine September 2022 Edition

CHAPTER TECHNICAL SESSIONS
PORT HARCOURT CHAPTER
Exploration activities in SPDC's Shallow Water
Offshore (SWO) acreage have focused on exploring for
gas bearing reservoirs in deep high-pressured
prospects to support NLNG T7 backfill. With increasing
depth and resultant deteriorating reservoir quality, it
becomes important to properly delineate the reservoirs
to enable the robust volumetric estimation. In trying to
solve this, we became increasingly aware of how much
risks and uncertainty exists. The de-risking work
required the need to properly integrate data and
technology to answer the key question: If the reservoirs are present and how good are
they in terms of reservoir quality?
With the recently acquired OBN Seismic Data providing a substantive framework, we set
out to understand the reservoir presence and its quality by integrating geological and
geophysical methods with the available legacy data. This resulted in appropriate
reservoir-seal pair definition, thickness ranges, NTG for prospective intervals, reservoir
chance factor and ultimately an improved definition of the subsurface. We look forward to
a successful drilling campaign which will also validate the learnings from this work in
uncalibrated Deep Plays, helping to open a new frontier for exploration and calibrate
parameters for subsequent exploration and development drilling.
Exploration and Production (E&P)organizations deal
with various data(geoscientific, engineering, structured
and unstructured data, etc.) of different sizes/types from
mixed sources.Each year, more data are acquired
and/or generated, recycled, retrieved, managed and
stored in silos or data lakes. As a result of corporate
data growth, the demand for more storage capacities
have been of tremendous increase. In parallel, sundry
challenges are being introduced and carried along
too.The include issues of streaming the data in a
centralized storage, issues of data security, data
quality/integrity, standardization, data exchange among different vendor applications,
and so on.It is interesting to note that different approaches have been offered in the past
by various organizations, however, the current dynamics in oil and gas data
management, occasioned with the Covid-19 pandemic impact requires a thorough and indepth
approach to managing these data. With the pandemic, organizations across the
industry have sought solutions to relieve the pressure. The importance of good Data
Management in the oil and gas industry cannot be over emphasized. Within the last few
decades, organizations unwilling to adopt the best practices are lagging.
This presentation, therefore, showcases varying solutions to the challenges above and
much more. It is expected that the audience will find value in the solutions discussed to the
extentof becoming obvious data management resource in their different organizations.
UK/EUROPE
CHAPTER
Petroleum geochemistry improves exploration
efficiency and production development
strategies. Petroleum geochemistry provides
insights in many of the variables that control the
volumes of crude oil and natural gas available for
entrapment, including source-rock distribution,
richness and quality, thermal maturity, the timing
of generation-migration-accumulation relative to
trap formation and prediction of type of
hydrocarbons. Petroleum Geochemistry is most powerful when used with other
disciplines, such as seismic sequence stratigraphy, structural geology, and
reservoir characterisation.
Petroleum geochemistry offers rapid, low-cost evaluation tools to aid in
understanding development and production problems. Petroleum reservoir
geochemistry provide (1) assessment and prediction of hydrocarbons quality
(2) evaluation of the processes that control hydrocarbon alteration in reservoirs
(2), (3) assessment of vertical and lateral fluid continuity in a reservoir, (4)
determination of proportions of commingled production from multiple zones
and leaky casing, and (4) prediction of gas/oil and oil/water contact locations.
A case study on the oil-oil and oil-source rock correlations is included in this
presentation.
Machine Learning (ML) is a growing technology
that has proven to be effective and efficient. This
research was aimed to devise ML models to
predict the lithology of North Sea wells from well
logs employing three (3) ML classifiers based on
an ensemble of decision trees (Random Forest,
Catboost and Xgboost). The dataset includes
well logs from 118 wells in the Northern North
Sea provided by the 2020 FORCE ML
competition. XGBoost outperformed other ML methods as it accurately
predicted more lithologies than other classifiers, with to total accuracy of 75%
predicting 89% & 66% of shale & sandstone correctly . The dataset's quality is
pivotal to the model's performance as poor data quality would output less
accurate models and vice versa.
Field XY is a mature oil field in the Niger Delta which has been on production since 1966. One of the major sustenance drivers for mature fields lies in
finding ways to improve oil production at minimal cost.
Bypassed oil was identified on producing reservoirs of Field XY using time-lapse cased-hole saturation logs. This was integrated with pressure and
production data to analyze the behavior of the reservoirs, identify bypassed oil, and select suitable candidates for intervention, workover & infill drilling.
Cased-hole Saturation Logging which utilizes pulsed neutron technology was key in investigating the presence of bypassed oil in the field. The tool was
logged in several Wells behind Casing and Completion tubulars. Two main acquisition modes were used: Carbon-Oxygen (C/O) and Sigma. The C/O
mode is hinged on the inelastic scattering of high energy neutrons by atomic nuclei resulting in the detection of atoms in the Formation. The ratio of
Carbon to Oxygen atoms provides information about Oil saturation. The Sigma log focuses on the capture of neutrons by atomic nuclei. The hydrogen
atom plays a significant role in this capture process and is thus linked to porosity and the current water saturation of the reservoir. The Sigma method is
sensitive to the salinity of the Formation and works better at higher salinities. The C/O method works independent of salinity.
The logging context was an environment of low Water salinity. The Sigma log gave good indication of Water saturation in Gas-bearing intervals but was not robust enough to discriminate
between Oil and Water due to low salinities. The C/O log was effective in computing Oil saturations.
Rigless interventions were successfully carried out in identified bypassed areas and infill drilling has been proposed based on the result of the cased-hole saturation logging and reservoir
synthesis.
NAPENEWS AUG/SEPT 2022 59