Minagish Oolite reservoir is a prolific limestone reservoir in Umm Gudair field underlain by an active aquifer situated in West Kuwait. The field has been on production for over 50 years and has been experiencing rising water production levels in the recent years. Understanding the movement of water in the reservoir is vital for maximizing oil recovery.
During the producing life of the reservoir, the vertical movement of water is influenced by presence of flow barriers / baffles in the reservoir and how they are distributed in the vertical as well as areal direction. Understanding the lateral distribution of the flow barriers to fluid movement in the vertical direction has been a challenge throughout the production history of the field. Efforts have been ongoing in the past, to understand the movement of aquifer water in the vertical direction based on analysis of openhole log data, structural configuration, stratigraphy, well performance, production logging (PLT) results etc. These have resulted in developing a respectable level of understanding of the distribution and strength of barriers/baffles and their effectiveness in the field performance.
In a recent campaign to reduce the rapidly increasing volume of water produced from Minagish Oolite reservoir, a large number of workovers were carried out based on the current understanding of the vertical barriers / baffles, resulting in bringing down the water-cut level appreciably. The paper analyzes the results obtained from carrying out the numerous workovers for water shut-off in the recent campaign. This analysis has been utilized in an attempt to improve the history match in the dynamic reservoir simulation, especially the water-cut history match. Whereas good match of long water-cut history before the recent water shut-off jobs indicates absence of serious issue of well integrity, transmissibility modifiers in the simulation model were required, in order to improve water-cut history match in the post water shut-off period. Thus, there is vast improvement in the simulation team's understanding of the lateral distribution and strength of barriers / baffles. This has greatly aided in the formulation of more pragmatic plans for future workovers involving water shut-off by squeezing-off or isolating watered out layers. The result is a more robust prediction of production profile from the future field development activities.
The paper presents how the integrated approach of the open-hole, cased hole logs data with field performance in the history match process of simulation helps in the improvement of reservoir simulation modeling.
Al-Shammari, Asrar (Kuwait Oil Company) | Gonzalez, Fabio A (BP Kuwait) | Gonzalez, Doris L (BP America) | Jassim, Sara (Kuwait Oil Company) | Sinha, Satyendra (Kuwait Oil Company) | Al-Nasheet, Anwar (Kuwait Oil Company) | Datta, Kalyanbrat (Kuwait Oil Company) | Younger, Robert (BP Kuwait) | Almahmeed, Fatma (Kuwait Oil Company)
Magwa-Marrat reservoir fluid is an asphaltenic hydrocarbon, exhibiting precipitation and deposition of asphaltene in the production system including the reservoir rock near wellbore and the tubing. The main objective of this work was to optimize production in Magwa-Marrat wells by remediation of tubing plugging and formation damage. Well interventions were prioritized based on potential production benefit resulting from the removal of productivity impairment. It was required to understand current formation damage in all wells, including those without recent pressure transient analysis (PTA).
All PTA tests since 1983 for Magwa-Marrat reservoir were analyzed to determine the different reservoir parameters such as flow capacity (KH), Skin (S), reservoir boundaries, and the extrapolated reservoir pressure (P*). PTA derived permeability was compared to log derived permeability to quality control skin determination. Independently formation damage was estimated using the radial form of the solution of the diffusivity equation for pseudo steady state flow. Once a skin correlation for both PTA vs. Darcy's law equation was derived using out of date well performance, the formation damage for all wells was accessed using current productivity index to identify production optimization opportunities in wells without recent PTA. This work was combined with nodal analysis to separate vertical lifting performance and inflow performance relationship impact on total productivity detriment.
Cross plot of PTA derived flow capacity (Kh) vs. Log derived Kh correlates very well with a slope and a coefficient of correlation close to 1.0. This was observed for wells located in the reservoir where there are not heterogeneities near wellbore such as boundaries or natural fractures. For these cases the higher than normally observed estimated skin explained poorer well productivity. After skin values were accessed for all wells, a production gain was estimated, and the wells were ranked based on potential benefit. A stimulation campaign was put in place based on the type of rock, formation damage and vertical lifting performance. Eight (8) wells were stimulated and they delivered approximately an additional 20% production for the field.
This work was innovative in the sense that there was not pressure build up tests run prior to the interventions and such, there was not any production deferral. This was achieved by building the well performance understanding on a correlation that required petrophysical description, production rates and estimates of drainage area reservoir pressure.
Al Jadi, Issa A. (Kuwait Oil Company) | Desai, Sameer Faisal (Kuwait Oil Company) | Al-Ghanim, Wafaa (Kuwait Oil Company) | Al-Wazzan, Roqaya M. (Kuwait Oil Company) | Al Sabea, Salem H. (Kuwait Oil Company) | Al Haddad, Saud M. (Kuwait Oil Company) | Franco, Francy Milena (Schlumberger) | Khor, Siew Hiang (Schlumberger) | Saxena, Aditya (Schlumberger) | Zhang, Qiong Michael (Schlumberger) | Hapsari, Hairuni Safri Tri (Schlumberger) | Elayaat, Ahmed A.Fouad. (Schlumberger) | Bodwadkar, Suhas V. (Schlumberger)
A proven and effective integrated asset modelling (IAM) approach has been adopted to bring multiple interdependent wells, pipelines networks, and process facilities models together into one single truly integrated asset model for the Greater Burgan Oilfield in Kuwait. The integrated wells-network facility models via the IAM platform also includes a water processing facility model which consists of 2 effluent water disposal plants; a crude oil export pipeline network and a water reinjection network model. This paper describes how a representative integrated asset model was developed for the Greater Burgan Oilfield through a model centric approach executed within an Integrated Operational Excellence (IOX) Program towards a Digital Transformation initiative by Kuwait Oil Company (KOC) South and East Kuwait (S&EK) Group together with Schlumberger. It also describes how this tool enables the asset teams to evaluate different operating scenarios to further enhance well performance and the overall asset productivity via rerouting well flow path to an appropriate header, identifying well workover opportunities, reevaluating artificial lift design, adding future wells (for field development) and comprehensive understanding of well integrity and flow assurance studies. The assessment was done not only at a gathering center (GC) level but also asset-wide level where the complete system constraints, interactions and back pressure effects between more than 2000 different wells were fully accounted. The simulated results such as pressure gradient, temperature gradient and erosional velocity ratio gradient across the production networks are presented on the GIS map for easy opportunity identification. The availability of this fully integrated asset model with up-to date calibrated wells and network models and process models enables KOC engineers to better understand current well performance and production potential, identify any possible bottlenecks imposed by the large complex surface network and process facilities of Greater Burgan Oilfield.
Desai, Sameer Faisal (Kuwait Oil Company) | Rane, Nitin M. (Kuwait Oil Company) | Al-Shammari, Baraa S. (Kuwait Oil Company) | Al-Sabea, Salem H. (Kuwait Oil Company) | Al-Naqi, Meqdad (Kuwait Oil Company)
Kuwait Oil Company initiatives for ushering in a new era of digital transformation of its assets to intelligently and optimally manage the Oil and Gas fields were successfully realized with the completion of three pilot projects entitled Kuwait Integrated Digital Fields (KwIDF). This paper discusses major achievements of the Digital Oilfield technology implemented in Burgan KwIDF project and provides an insight on the challenges in operating it.
The Burgan KwIDF pilot successfully transformed GC-1 production asset into a fully instrumented DOF comprising of digital instruments and infrastructure installed at well site and the production facility. Real-time production data is transmitted to a state of the art collaboration center that integrates data continuously with automated workflows for validation, modeling and tuning of well and facility models. Right time decision support information generated from smart visualization tools allow quick actions for production optimization, well and facility management in a collaborative work environment.
There is persistent value realization from KwIDF technology implemented in Burgan field. It has generated substantial cost savings with faster response time in restoring production and reduction in non-productive time. Driven by the digital environment asset production has sustained at target as production gain opportunities are capitalized and losses compensated quickly.
Over the period of time with experience in utilizing the DOF technology it has been observed that the technology sustainment is dependent on the technology providers to a large extent. The main components that require their continuous support are the digital instruments, proprietary software, hardware and related infrastructure. Technical expertise in each domain is necessary for ensuring continuous and smooth operations in the field, wellsite and collaboration centers. Development of an integrated team of domain experts is crucial for successfully managing the DOF operations. Change management initiatives for developing an in house user champion team is mandatory for ensuring sustainment. The important lessons learned and solutions are discussed in detail.
The Middle cretaceous Wara sandstone reservoir in Minagish Field is considered as highly heterogenetic sandstone which implying lateral facies extensive variations, stacked sand bodies with varying petrophysical properties. Several horizontal wells has been successfully drilled in lower part of Wara 6 sand channel, best thicker clean sand channel with very good oil production rate. Recently some wells have shown depleting of oil and increase water production. To develop such a challenging reservoir to maximize the oil production, a new plan has been developed to explore for new opportunities in Wara reservoir. The objective is to target different good stacked sand bodies in different Wara layers by drilling deviated wells. Some of old depleted Minagsih Oolite reservoir wells have shown good opportunities to sidetrack the wells into good Wara sand layers. This paper presents the integration between geostatistical models, well logs, well test results and different seismic elastic properties maps to identify best subsurface locations for drilling new deviated wells which combine the best quality sand bodies in different Wara layers. A few years ago geostatistical reservoir model along with core data and well log data were utilized to drill successful horizontal wells in W6 sand channels. However due to low resolution seismic data, Wara highly heterogonous lithology and uncertainty in geo-statistical model, it was challenging to continue identify good quality stacked sand bodies in different Wara layers without drilling unwanted silty sand or shale layers. Seismic inversion related elastic impedance data could discriminate between the good quality oil-bearing sand, shaly, and silty sandstones. Several old vertical wells that include good stacked sand bodies in different Wara layers; have been selected to validate the accuracy of elastic impedance maps along Wara layers.
We interpreted a series of single-well-chemical-tracer-tests (SWCTTs) estimating residual oil (SORW) to base high salinity waterflood, low salinity waterflood and subsequent polymer flood conducted on a Greater Burgan well. Interpretation of the tests requires history matching of the back-production of partitioning and non-partitioning tracers which is impacted by differing amounts of irreversible flow and differing amounts of dispersion as well as the amount of residual oil.
We applied the state-of-the-art chemical reservoir simulator (UTCHEM) and an assisted history matching tool (BP’s Top-Down-Reservoir-Modeling) to interpret the tests and accurately quantify uncertainty in residual oil saturations post high salinity, low salinity, and polymer floods. Two optimization algorithms (i.e., Genetic algorithm (GA) and Particle-Swarm-Optimization (PSO)-Mesh-Adaptive-Direct-Search (MADS) algorithms) were applied to better address the uncertainty.
Our results show a six saturation unit decrease in SORW post low salinity with no change to the SORW post polymer. This is in-line with our expectations - we expect no change in SORW post-polymer as the conventional HPAM, which does not exhibit visco-elastic behavior, was used in the test. We demonstrate that history matching the back-produced tracer profiles is a robust approach to estimate the SORW by showing that three-or four-layer simulation model assumption does not change the SORW estimated. We accounted for the uncertainty in partition-coefficient in our uncertainty estimates.
We present several innovations that improve history matching back-produced tracer profiles; hence, better SORW estimations (e.g., different level of dispersivity for individual simulation layers to account for different heterogeneity level as opposed to assuming a single dispersion for all layers). We generate more robust estimates of uncertainty by finding a range of alternative history matches all of which are consistent with the measured data.
Desai, Sameer Faisal (Kuwait Oil Company) | Al Jadi, Issa (Kuwait Oil Company) | Al-Ghanim, Wafaa (Kuwait Oil Company) | Al Sabea, Salem H. (Kuwait Oil Company) | Al-Haddad, Saud M. (Kuwait Oil Company) | Franco, Francy Milena (Schlumberger) | Khor, Siew Hiang (Schlumberger) | Bodwadkar, Suhas V. (Schlumberger) | Saxena, Aditya (Schlumberger) | Zhang, Qiong Michael (Schlumberger) | Hapsari, Hairuni Safri Tri (Schlumberger) | Morales, Fernando (Schlumberger) | Halabe, Vijaya (Schlumberger)
Several efforts have been made in the past for generating an Integrated Asset Model (IAM) for the Greater Burgan field in Kuwait with mixed results on sustained utilization and benefits. A new effective full field Integrated Asset Model has now been developed within an Integrated Operational Excellence (IOX) program towards Digital Transformation of the Greater Burgan field. A proven model centric approach has been adopted to bring multiple interdependent wells, pipelines networks, and process facilities models together into one single truly integrated asset model. The IAM platform also includes a water processing facility model which consists of 2 effluent water disposal plants, a crude oil export pipeline network and a water injection network model. Development of this integrated wells-network-facility-crude export-water processing facility-water injection network model incorporating the 14 gathering centers in the South and East Kuwait (SEK) asset focused on providing all the essential valuable inputs to business processes for better asset management, faster and more accurate decision-making and optimizing the hydrocarbon flow path all the way from the reservoir till the export point. The assessment was done at full field level where the complete system constraints, interactions and back pressure effects between more than 2000 different wells were fully accounted up to the crude processing facilities. The availability of this fully integrated asset model with up-todate calibrated wells and network models and process models enables Kuwait Oil Company (KOC) engineers to better understand current well performance and production potential, identify any possible bottlenecks imposed by the large complex surface network and process facilities of Greater Burgan Oilfield. The simulated results such as pressure gradient, temperature gradient and erosional velocity ratio gradient across the production networks are presented on the GIS map for easy opportunity identification.
Xu, Wei (CNOOC Research Institute Co., Ltd.) | Chen, Kaiyuan (Beijing Key Laboratory of Unconventional Natural Gas Geological Evalution and Development Engineeing, China University of Geosciences Beijing) | Fang, Lei (Beijing Key Laboratory of Unconventional Natural Gas Geological Evalution and Development Engineeing, China University of Geosciences Beijing) | Zhang, Yingchun (CNOOC Research Institute Co., Ltd.) | Jing, Zhiyi (CNOOC Research Institute Co., Ltd.) | Liu, Jun (CNOOC Research Institute Co., Ltd.) | Zou, Jingyun (CNOOC Research Institute Co., Ltd.)
The lacustrine delta sandbody deposited in the north of Albert Basin is unconsolidated due to the shallow burial depth, which leads to an ultra-high permeability (up to 20 D) with large variation and poor diagenesis. Log derived permeability differs greatly with DST results. Thus, permeability simulation is challenging in 3D geomodeling. A hierarchical geomodeling approach is presented to bridge the gap among the ultra-high permeability log, model and DST results. The ultimate permeability model successfully matched the logging data and DST results into the geological model.
Based on the study of sedimentary microfacies, the new method identifies different discrete rocktypes (DRT) according to the analyis of core, thin section and conventional and special core analysis (e.g., capillary pressure). In this procedure, pore throat radius, flow zone index (FZI) and other parameters are taken into account to identify the DRT. Then, hierarchical modeling approach is utilized in the geomodeling. Firstly, the sedimentary microfacies model is established within the stratigraphic framework. Secondly, the spatial distribution model of DRT is established under the control of sedimentary microfacies. Thirdly, the permeability distribution is simulated according to the different pore-permeability relation functions derived from each DRT. Finally, the permeability model is compared with the logging and testing results.
Winland equation was improved based on the capillary pressure (Pc) data of special core analysis. It is found that the highest correlation between pore throat radius and reservoir properties was reached when mercury injection was 35%. The corresponding formula of R35 is selected to calculate the radius of reservoir pore throat. Reservoirs are divided into four discrete rock types according to parameters such as pore throat radius and flow zone index. Each rock type has its respective lithology, thin section feature and pore-permeability relationship. The ultra-high permeability obtained by DST test reaches up to 20 D, which belongs to the first class (DRT1) quality reservoir. It is located in the center of the delta channel with high degree of sorting and roundness. DRT4 is mainly located in the bank of the channels. It has a much higher shale content and the permeability is generally less than 50 mD. Through three-dimensional geological model, sedimentary facies, rock types and pore-permeability model are coupled hierarchically. Different pore-permeability relationships are given to different DRTs. After reconstructing the permeability model, the simulation results are highly matched with the log and DST test results.
This hierarchical geomodeling approach can effectively solve the simulation problem in the ultra-high permeability reservoir. It realizes a quantitative characterization for the complex reservoir heterogeneity. The method presented can be applied to clastic reservoir. It also plays a significant positive role in carbonate reservoir characterization.
One of the goals cherished by modern organizations is gender balance, as a proven way to enhance productivity, boost the motivation of employees and enrich the leadership pipelines of internal succession plans. The energy sector follows closely this trend, inclusive of major organizations of operations and services, especially in Oil and Gas. And in no other region of the world this is now more visible than in the Middle East, one of the most active and traditionally leading and strategic regions in the segment. This paper summarizes what factors were fundamental for the very visible blooming of the female leadership, particularly in the oil and gas sectorin the Gulf Cooperation Council GCC countries. Certainly, middle-eastern women do not account yet for a large or representative number inthe highest roles of private or National Oil Companiesof their countries, but things are rapidly changing, and the blooming is real.
A comparison of key elements considered diagnostic about the empowerment of women, like female workforce percentages, gender gap indexes, and representation of women in leadership roles in oil and gas are herein analyzed for the GCC, comparing those with figures of other regions of the World. Other indicators were included in the analysis, which proved to be key for developing women's leadership, in particular, communication strategies, empowering plans, training, active and visible endorsement of top leaders and other strategies of governmental agencies and corporations. Definitively, the Middle East, and particularly the GCC countries, in which our study is centered, have boldly address cultural issues and traditional barriers, to produce step-changes that are quickly transforming the oil and organizations in all countries of the region. A forecast of opportunities for women's leadership in the upstream and downstream sectors of the oil industry in the future is proposed, in a story of learned best practices worth sharing.
The paper includes a summary of the standing and utilization of social media channels by main organizations in oil and gas. A frame of the current trends analyzed resulted in the identification of the organizations more successful in the utilization of these key channels, so relevant for the general audiences and the new generations. Some unexpected findingsshaped our conclusions about strategies instrumental for step-changes needed in political or cultural settings that may be challenging for boosting women's empowerment.
Downhole fluid sampling is ubiquitous during exploration and appraisal because formation fluid properties have a strong impact on field development decisions. Efficient planning of sampling operations and interpretation of obtained data require a model-based approach. We present a framework for forward and inverse modeling of filtrate contamination cleanup during fluid sampling. The framework consists of a deep learning (DL) proxy forward model coupled with a Markov Chain Monte Carlo (MCMC) approach for the inverse model.
The DL forward model is trained using precomputed numerical simulations of immiscible filtrate cleanup over a wide range of in situ conditions. The forward model consists of a multilayer neural network with both recurrent and linear layers, where inputs are defined by a combination of reservoir and fluid properties. A model training and selection process is presented, including network depth and layer size impact assessment. The inverse framework consists of an MCMC algorithm that stochastically explores the solution space using the likelihood of the observed data computed as the mismatch between the observations and the model predictions.
The developed DL forward model achieved up to 50% increased accuracy compared with prior proxy models based on Gaussian process regression. Additionally, the new approach reduced the memory footprint by a factor of ten. The same model architecture and training process proved applicable to multiple sampling probe geometries without compromising performance. These attributes, combined with the speed of the model, enabled its use in real-time inversion applications. Furthermore, the DL forward model is amendable to incremental improvements if new training data becomes available.
Flowline measurements acquired during cleanup and sampling hold valuable information about formation and fluid properties that may be uncovered through an inversion process. Using measurements of water cut and pressure, the MCMC inverse model achieved 93% less calls to the forward model compared to conventional gradient-based optimization along with comparable quality of history matches. Moreover, by obtaining estimates of the full posterior parameter distributions, the presented model enables more robust uncertainty quantification.