Mauddud Formation is a major oil-producing reservoir in Raudhatain Field of North Kuwait. The Mauddud Formation is an early Albian in age and it was generated an environment of the shallow-water carbonate and consists of Grainstones, Wackestones and Mudstones deposited in ramp settings. In Raudhatain field (RAMA) is undertaking massive development efforts with planned enhancement in Oil production. Reservoir description and distribution of rock properties in 3D space are challenging due to inherent reservoir heterogeneity, in this case primarily driven by depositional and diagenetic patterns.
KOC North Kuwait Reservoir Studies Team (NK RST) has been challenged to increase the production from several key NK oil fields. To achieve this goal, KOC has partnered with Schlumberger to rebuild integrated model with Petrophysics, Geophysics, and Geology and Reservoir data of the Mauddud Reservoir. The original model was required to minimize challenges in new infill locations, increase Oil recovery factor and detect water breakthrough to minimize water production. One of the key issues in creating RAMA reservoir model is integration of all available data in identifying the horizontal permeability, reservoir heterogeneity and identification of thief zones.
A fine Geological grid model with 35M cells, 10 Geological horizons has been built to characterize the Mauddud reservoirs of the RAMA field including the permeability from PLT logs combined with petrophysical and lithological / facies data to add more understanding of the distribution of reservoir properties. Log response group methodology and the undeveloped area in the Saddle (structurally low area) has been modelled for the first time in Raudhatain NK Field. This combined study utilizes the available data and cutting-edge technology using Geo2Flow which resulted in fluid compartmentalization and free water level identification. STOOIP has been upgraded and unlocking potential in new segments of the developed field. The original model was built based on vertical/Deviation wells (345) which lead to discrepancies in the structural interpretation. The new update has been carried out including all horizontal wells to minimize the uncertainty in the structure framework.
Many investigations have been discussed and it is a well-recognized fact that sonic wave velocity is not only influenced by its rock matrix and the fluids occupying the pores but also by the pore architecture details of the rock bulk. This situation still brings a lack of understanding, and this study is purposed to clearly explain how acoustic velocity and quality factor correlate with porosity, permeability and details internal pore structure in porous rocks.
This study employs 67 sandstone and 120 carbonate core samples collected from several countries in Europe, Australia, Asia, and USA. The measured values are available for porosity
At least eight rock groups are established from rock typing with its Kozeny constant. This constant is a product of pore shape factor
As a novelty, the empirical equations are derived to estimate compressional velocity and quality factor based on petrophysical parameters. Furthermore, this study also establishes empirical equations for predicting porosity and permeability by using compressional wave velocity, critical porosity, and PGS rock typing.
Al-Maqtari, Ameen N. (SAFER E&D Operations Company) | Saleh, Ahmed A. (SAFER E&D Operations Company) | Al-Haygana, Adel (SAFER E&D Operations Company) | Al-Adashi, Jaber (SAFER E&D Operations Company) | Alogily, Abdulkhalek (SAFER E&D Operations Company) | Warren, Cassandra (Schlumberger) | Mavridou, Evangelia (Schlumberger) | Schoellkopf, Noelle (Schlumberger) | Sheyh Husein, Sami (Schlumberger) | Ahmad, Ammar (Schlumberger) | Baig, Zeeshan (Schlumberger) | Teumahji, Nimuno Achu (Schlumberger) | Thiakalingam, Surenthar (Schlumberger) | Khan, Waqar (Schlumberger) | Masurek, Nicole (Schlumberger) | Andres Sanchez Torres, Carlos (Schlumberger)
A 3D petroleum systems model (PSM) of Block 18 in the Sab'atayn basin, onshore western Yemen, was constructed to evaluate the untapped oil and gas potential of the Upper Jurassic Madbi formation. 3D PSM techniques were used to analyze petroleum generation for conventional reservoirs and the petroleum saturations retained in the source rock for the unconventional system. Block 18 has several proven petroleum systems and producing oil and gas fields. The principal source rocks are within the Madbi Formation, which comprises two units, the Lam and the Meem members. Both contain transgressive organically rich "hot" shales with total organic carbon (TOC) of 8 to 10%; these are located stratigraphically at the base of each member. Additional organic-rich intervals within the Lam and Meem are less-effective source rocks, with lower TOC values.
The PSM consisted of 17 depositional events and 2 hiatuses. To accurately replicate geochemical and stratigraphic variations, the Lam and Meem members were further divided into sublayers. The model was calibrated to present-day porosity, permeability, and pressure data, and it incorporated vertical and lateral lithofacies and organic facies variations. Further calibrations used observed maturities (vitrinite reflectance and pyrolysis Tmax) and present-day temperatures and considered laterally variable heat flow from the Early Jurassic to the Late Miocene. Finally, petrophysical analyses from wells provided calculated hydrocarbon saturations, which were used to calibrate the saturation output from the model. The model satisfactorily reproduces the distribution of the main gas and oil fields and discoveries in the study area and is aligned with well test data.
Maturity results indicate that the upper Lam intervals currently sit within the main to early oil window but are immature at the edges of Block 18 (based on the Sweeney and Burnham Easy R0% kinetics). The lowest Lam unit enters the wet gas window in the center of the block. The underlying Meem member ranges from wet gas to early oil window maturity. Like the Lam, the Meem remains immature along the edges of Block 18. However, in the south of the block, the richest source rocks within the Meem are mainly in the oil window. The degree of transformation of the Meem and Lam varies throughout the members. The model predicts that, at present, the lowest part of the Meem, containing the greatest TOC, has 90% of its kerogen transformed into hydrocarbons.
The model confirms that the Madbi formation is a promising unconventional shale reservoir with a high quantity of hydrocarbons retained within it. Despite the higher quantity of hydrocarbons retained in the upper Meem, in terms of liquid and vapor hydrocarbons predicted in this model, the lower Lam is the most-prospective conventional tight sand reservoir, and the Meem has very small potential as tight sand reservoirs. This study provided a novel application of 3D PSM technology to assess new unconventional as well as conventional plays in this frontier area.
Ho, Yeek Huey (Petroliam Nasional Berhad, PETRONAS) | Ahmad Tajuddin, Nor Baizurah (Petroliam Nasional Berhad, PETRONAS) | Elharith, Muhammed Mansor (Petroliam Nasional Berhad, PETRONAS) | Dan, Hui Xuan (Petroliam Nasional Berhad, PETRONAS) | Chiew, Kwang Chian (Petroliam Nasional Berhad, PETRONAS) | Tan, Kok Liang (Petroliam Nasional Berhad, PETRONAS) | Tewari, Raj Deo (Petroliam Nasional Berhad, PETRONAS) | Masoudi, Rahim (Petroliam Nasional Berhad, PETRONAS)
Managing a 47-year brownfield, offshore Sarawak, with thin remaining oil rims has been a great challenge. The dynamic oil rim movement has remained as a key subsurface uncertainty especially with the commencing of redevelopment project. A Reservoir, Well and Facilities Management (RWFM) plan was detailed out to further optimize the development decisions. This paper is a continuation from SPE-174638-MS and outlines the outcome of the RWFM plan and the results’ impact towards the development decisions, such as infill well placement and gas/water injection scheme optimization. Key decisions impact by the RWFM findings are highlighted.
One of the RWFM plans is oil rim monitoring through saturation logging to locate the current gas-oil contact (GOC) and oil-water contact (OWC). Cased-hole saturation logs were acquired at the identified observation-wells across the reservoir to map time-lapse oil rim movement and its thickness distribution. Pressure monitoring with regular static pressure gradient surveys (SGS) as well as production data, helped to understand the balance of aquifer strength between the Eastern and Western flanks. Data acquisition opportunity during infill drilling were also fully utilized to collect more solid evidences on oil rim positions, where extensive data acquisition program, including conventional open-hole log, wireline pressure test, formation pressure while drilling (FPWD) and reservoir mapping-while-drilling, were implemented.
The timely collection, analysis and assimilation of data helped the team to re-strategize the development / reservoir management plans, through the following major activities: Re-strategizing water and gas injection plan to balance back oil rim between the Eastern and Western flanks, through deferment of drilling water injectors, optimization of water and gas injectors location and completion strategies due to stronger aquifer encroachment from east and south east. Optimizing infill wells drainage points where 2 wells were relocated based on cased-hole logs, as the first well original location was swept and the second well was successfully navigated through the oil rim using reservoir mapping-while-drilling techniques coupled with cased-hole log results. This resulted in securing an oil gain of 4000 BOPD from these 2 wells. Optimizing infill wells location and planning an additional infill well with potential additional oil gain of approximately 2000 BOPD. The understanding of current contact and aquifer strength from the surveillance data assisted in identifying fit-for-purpose technology for the new wells such as the application of viscosity-based autonomous inflow control device which assisted in placing the well closer to GOC due to the observed rapid rising of water table, this will help sustaining the well life.
Re-strategizing water and gas injection plan to balance back oil rim between the Eastern and Western flanks, through deferment of drilling water injectors, optimization of water and gas injectors location and completion strategies due to stronger aquifer encroachment from east and south east.
Optimizing infill wells drainage points where 2 wells were relocated based on cased-hole logs, as the first well original location was swept and the second well was successfully navigated through the oil rim using reservoir mapping-while-drilling techniques coupled with cased-hole log results. This resulted in securing an oil gain of 4000 BOPD from these 2 wells.
Optimizing infill wells location and planning an additional infill well with potential additional oil gain of approximately 2000 BOPD.
The understanding of current contact and aquifer strength from the surveillance data assisted in identifying fit-for-purpose technology for the new wells such as the application of viscosity-based autonomous inflow control device which assisted in placing the well closer to GOC due to the observed rapid rising of water table, this will help sustaining the well life.
This paper highlights the importance of data integration from geological knowledge, production history, reservoir understanding and monitoring through regular SGS and time-lapse cased-hole saturation logging, coupled with extensive data acquisition during infill drilling. By analyzing and integrating the acquired data, project team can then confidently re-strategize and successfully execute the complex mature oil-rim brownfield redevelopment.
A. H. Khan, M. Faisal (Pakistan Petroleum Limited) | Abid, M. Faraz (Pakistan Petroleum Limited) | Fareed, Abdul (Pakistan Petroleum Limited) | Javed, Zeeshan (Pakistan Petroleum Limited) | Khan, M Noman (Pakistan Petroleum Limited) | Hashmi, Shariq (Pakistan Petroleum Limited)
Technical evaluation and subsequently devising an appraisal and development strategy of a structural cum stratigraphic reservoir based on a discovery well only is always challenging. The reservoir under discussion was discovered as a structurally bounded trap and the appraisal wells were drilled on NW-SE direction along with the main bounding fault based on this understanding. However, presence of hydrocarbon below the spill point, anomalous sand thickness, lateral facies and reservoir quality variations observed in few of the wells indicated stratigraphic component in the field. Further complexity was added when the deepest tested gas was assigned on the structural map which showed extension of the hydrocarbon play outside the block boundary where the area was under different operating company that later drilled multiple wells near the block boundary. Therefore, it was critical to estimate correct initial gas in-place and percentage distribution of hydrocarbon across the lease boundaries.
Well location map for the studied field
Well location map for the studied field
The objective of this paper is to present workflow that integrates multiple dataset to understand the field's hydrocarbon filling mechanism. Detailed geophysical and Petrophysical work has been carried out, which includes building of sequence stratigraphic framework, preparation of seismic attribute maps, understanding of the depositional setting for all the individual sand units encountered in all the wells, rock quality assessment (core and log methods with integration of capillary pressure curves), free water level (FWL) assessment, permeability modelling using machine learning approach (NN), pore throat radius estimation to relate hydrocarbon filling mechanism and saturation-height function modelling to build consistent 1D water saturation model.
Comprehensive dataset has been acquired to evaluate the potential of the field that covers 3D seismic for the entire field, biostratigraphic analysis for seven (7) well, conventional logs in twelve (12) wells and advance measurements like Elemental Capture Spectroscopy and high-resolution resistivity images in five (5) wells. Core analysis data also acquired in five (5) different wells including routine core analysis, capillary pressure measurements using high pressure mercury injections, pore throat radius, relative permeability measurements (Centrifuge), formation resistivity factor measurements and sedimentological analysis (XRD & thin section) to overcome the challenges and defining the uncertainty associated with initial gas in-place.
Sequence based boundaries were defined to correlate individual sand bodies using the core data, image logs, elastic logs, seismic transacts and attribute maps for understanding the depositional setting. Lat-er these correlations were used to build a consistent petrophysical model including VCL estimation from Gamma/Neutron-Density/Sonic Density methods which was validated with ECS/XRD data. Porosity model was developed and validated from the core porosity followed by variable "m" estimation from the porosity/m relationship using the SCAL data. Later on, the consistent water saturation (Sw) models were built for all the studied wells. Permeability models were built using Neural Network (NN) where core-based permeability used for calibration and the model was tested qualitatively with the mobility and the well test permeability. For the validation of Sw from the logs, capillary pressure-based flow units were built using FZI/RQI, Winland & BVW (log) methods to define flow units defined through the core data. It was observed that the Winland R35 method-based pore throat radius had good correlation with the Sw log. FWL from MDT to estimate the height of the gas column, Skelt Harrison equation to capture the shape of the capillary pressure curve and Swi from the Centrifuge analysis were used to calibrate MICP end point which helped in building consistent Saturation-height functions. Results showed good to excellent match from the modeled Sw (Pc) vs Sw(log).
Rajput, Sanjeev (Petronas Carigali Sdn Bhd) | Bt Abdullah, Irmawaty (Petronas Carigali Sdn Bhd) | Roy, Amit (Petronas Carigali Sdn Bhd) | B. Khalid, Aizuddin (Petronas Carigali Sdn Bhd) | Onn, Camellia (Petronas Carigali Sdn Bhd) | Khalil, Ashraf (Petronas Carigali Sdn Bhd)
Low electrical resistivity and low contrast reservoirs (LRLC) pay zones are composed of thinly-bedded laminated layers containing hydrocarbon accumulations surrounded by non-reservoir layers indicating lack of resistivity contrast. These pay zones are difficult to be distinguished at seismic and log scale due to lower vertical and lateral resolution. Traditionally, deep-resistivity logs in LRLC zones read 0.5 to 5 ohm-m. Low contrast pay zone occurs mainly when the formation waters are fresh or having low salinity resulting in a very little resistivity contrast between oil and water zones. Major challenges imposed in LRLC reservoirs include identification, characterization, and evaluation of the hydrocarbon interval, which is usually masked by the lack of resistivity contrast between the hydrocarbon and water zones. The identification and characterization of the lowdown on resistivity pay is essential for the re-development of mature assets for improved oil recovery. This paper deals with the characterization of low resistivity hydrocarbon-bearing thinly-bedded reservoirs from a brownfield.
To unlock the hidden potential of LRLC pay sands in the offshore Sarawak Malaysia, the effective integration of subsurface disciplines including petrophysics, geology and quantitative derivatives from the seismic analysis is vital. This study covers the geological perspective of low contrast reservoirs from an offshore oil field deposited in lower coastal plain settings located within offshore Sarawak Malaysia. An improved understanding of the geological, petrophysical and geophysical parameters was achieved by adopting a holistic and multidisciplinary approach. This includes the integration of core, logs, rock physics modeled parameters, stratigraphic, depositional and lithofacies information along with stochastic inversion derivatives. Acoustic Impedance shows the facies changes in broader terms between producing and non-producing zone.
The paper quantifies rock physics parameter uncertainties for LRLC pay zones and establishes a framework for LRLC reservoir characterization. Stochastic inversion derived P-Impedance and Vp/Vs ratio are used to predict fluid and facies probabilities (
Identified LRLC reservoirs proved to be of commercial-quality and increased oil production to the extent of several hundred thousands of barrels over the years and currently producing. Rock physics modeled parameters including AI and Vp/Vs are sensitive to LRLC pay zones and their effective integration with image logs, lithofacies, and seismic inversion lead to reduce uncertainties in infill drilling programs. Geological understanding of the possibility of LRLC occurrences is required to assess oil and gas bypassed oil. Detailed geological features are clearly resolved in high-definition image logs. Low resistivity pay zones present in the main reservoir intervals can be identified by integrating the information from low gamma ray, low impedance, and low resistivity zones collectively. The results of this study show the value of integrated approaches and improvements in reservoir description from stochastic inversion into reservoir models.
Accurate estimation of mud weight (MW) helps to conserve wellbore stability in real-time drilling operations. Determination of proper MW requires a correct understanding of the stress field, natural fractures, pore pressure, rock strength, borehole trajectories, etc. It is a problematic task especially in, highly inclined wells, deviated wells, and near salt formations due to uneven variations in wellbore stresses. Proper MWs are difficult to apply at target depths of the unstable formations because of uncertainties existing inside the wellbore. There are no reliable tools or techniques available that can precisely determine the optimum value of MW. This paper proposes a novel and more convenient approach to estimate the safe MW for deviated wells using surface measured data. In this study, Bagging and Random forest ensembles have been utilized to model the relationship between sensors measured variables and MW. The proposed framework has been trained and tested on real-time Norwegian post-drilling data. Artificial neural networks (ANNs) and support vector regression (SVR) have also been utilized in this study for comparison purposes. The analysis of prediction results clearly reveals that Random forest ensemble has acquired the highest coefficient of correlation and minimum estimation errors. The performance of Ensemble methods is found to be superior to the ANNs and SVR models. The proposed approach can be useful for the determination of MW required at different depths of reservoir formation and maintaining the wellbore stability during real-time operations.
Field presented here is located in offshore Abu Dhabi, consisting of multi-stacked reservoirs with different fluid and reservoir properties. In this paper, field development plan of one of reservoir has been presented which was initially planned to be developed with pattern water injection by more than 50 horizontal wells penetrating all the ten oil bearing layers from 9 well head towers. Reservoir consists of under-saturated oil with low gas-oil ratio and low bubble point. Initial 2 years of production was considered as Early Production Scheme (EPS period), during which significant amount of early production data consisting of downhole pressure measurement, time-lapse MDT, vertical interference data, PLT have been collected. Based on EPS data simulation model has been updated. Simulation fits well with the observed pressure gauge and time-lapse MDT data. Updated model gives good prediction for a year of blind test data (including saturation, MDT and porosity) collected from different wells several kilometers away from current development area reflecting a high level of confidence in areal and vertical connectivity representation. Considering other reservoir uncertainties different Development plans have been screened using updated model in order to improve recovery factor and economics. Based on development plan screening study, optimized development option has been chosen for Full Field Development.
This challenging reservoir characterization case study is defined by the interaction between two reservoirs with different production mechanisms: a fractured basement reservoir and an overlying sandstone reservoir. The existing static geologic concept has been significantly enhanced by integrating pressure data from a unique three-year shut-in period to aid modeling of fractured reservoir connectivity. Previously, the seismic dataset was predominantly used to model the fault and fracture network and guide well planning. In the current approach, the full field data set, including all drilling parameters and new reservoir surveillance data were integrated to address uncertainty in the connected hydrocarbon volume and the relative importance of each production mechanism. The result is a reservoir management tool with which to test re-development concepts and effectively manage pressure decline and increasing gas/oil ratio (GOR) and water production.
To achieve a fully integrated history matched model, the first step was to make a thorough review of the existing detailed seismic interpretation, vintage production logging tool runs (PLT's), wireline logs (including borehole image logs (BHI)) and drilling data to find a causal link between hydraulically conductive fractures and well production behavior. In parallel, a material balance exercise was run to incorporate the new pressure data acquired during the field's shut-in period. The results of the material balance analysis were combined with seismic and well data to define the distribution of connected fractures across the field. Additionally, the material balance analysis was used to determine the connected hydrocarbon volume, the distribution of initial oil in-place and the relative hydrocarbon contribution from each production mechanism.
The field is covered by multi-azimuth 3D seismic and 43 vertical to highly deviated development wells, providing significant static and dynamic data for characterizing the distribution of connected fractures. Despite this high quality, diverse and field-wide dataset, prior modeling iterations struggled to sufficiently describe the production behavior seen at the well level. This has resulted in a major challenge to predicting the production behavior of new development wells and planning for reservoir management challenges. Capturing the complex interaction between production variables (including lithology, matrix versus fracture network, geomechanical stresses, reservoir damage and pressure depletion) at a field level instead of at an individual well level resulted in a unified static and dynamic model that reconciles all scales of observation.
This oilfield represents a unique reservoir characterization opportunity. The result is a key example of how iterative, integrated geological and engineering driven reservoir modeling can be used to inform the development in a complex, mature field. This case study provides an excellent analogue for the reservoir characterization of other fractured Basement fields and/or Basement-cover reservoir couplet fields in the early to late phases of their development.
Numerous carbonate reservoir discoveries were made in Indonesia (
The process involves multiple cycles—from formation evaluation (e.g., geomechanics analysis, design of an effective fracturing method, and production forecasting) through the economic impact to the operator. During the early phase of this integrated study, the uncertainties of all static and dynamic parameters (i.e., geological complexity, rock physics, and stress profile) were considered for fracturing design. Production performances from multiple fracturing stimulation scenarios were then modeled and compared to select the plan that optimizes production for the Berai Formation.
Results demonstrated an effective multidiscipline approach toward a comprehensive strategy to meet the ultimate objective in optimizing production. This project leveraged formation evaluation and fracturing design to deliver integrated solutions from exploration to accurate production forecast. The well stimulations were performed by carefully selecting fluid characteristics based on geological-petrophysical properties, pressure, and stress profiles within the area. Results yielded excellent production gains—for the best case, up to 50% with an average of 40% in comparison with initial production by using an acid that provides optimum fracture geometry and permeability.
This opportunity demonstrated the importance of understanding formation behavior and the parameters that aid the selection of an appropriate fracturing design for a low porosity/permeability carbonate reservoir.