Kuwait Oil Company

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.

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.

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.

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.

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.

Kuwait Oil Company (KOC) is going through many new challenging projects that aim to increase its hydrocarbons production capacity by 70%. The North Kuwait Jurassic Gas Fields project is one of the key projects with unique challenges from the subsurface complex and challenging characteristics of deep reservoirs, high pressure high temperature (HPHT), high in H2S and CO2 concentration-to the design, construction and operating of surface facilities. The Gas Field Development (GFD) group was established in 2007 to manage and accomplish KOC’s desired objectives from the NKJ Gas Fields project. The new group had to recruit manpower and build the required technical skills to address the unique challenges. End of 2010, KOC-GFD entered into an Enhanced Technical Service Agreement (ETSA) with Shell in order to benefit from the International Oil Company (IOC) expertise. One of ETSA objectives is to develop local KOC staff through Knowledge Transfer, whereas challenge was more than 60% of the total GFD population were juniors, i.e. less than 4 years of experience.

The need to fast track the development of the new recruited staff was identified by the management as a critical key element to overcome the project complex challenges. The development of a new approach for staff development using the best of both worlds’ i.e. building on KOC’s training programs and supplementing with Shell Jurassic ETSA Knowledge Transfer resulted in the creation of the Technical Competence Ladder, TCL, framework for all GFD staff in 2017. This technical paper will describe how the Jurassic ETSA Knowledge Transfer –

progressed over the course of the contract;

The results include the development of Structure and detailed competence skills development program for main subsurface disciplines like: Reservoir Engineering, Petrophysics, Geosciences, & Petroleum Engineering. Each main discipline includes number of specialization and focused sub-programs. The TCL program was implemented, and the Knowledge Transfer are proven. The progress of junior staff competences has been tracked and measured over the years; the creation of motivated and competent workforce has resulted in improved performance and increased team productivity. The overall results reduced ‘existing’ competency gaps within the company, enhanced communication between junior and senior staff, improved staff confidence and work performance. Key examples of success will illustrate the points covered in the technical paper.

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.

Crude oil production in Um-Ghdair field is consider one of the most complex operational activities in Kuwait Oil Company due to high water cut percentage, asphaletene flocculation, high viscosity and tight emulsion phenomena. As the fluid travels through the reservoir, wellbore, flowline, all the way to the gathering center, the state of initial equilibrium is disturbed leading to change in the chemical composition of the crude oil. As pressure and temperature continue to drop, and gas escapes, more asphaltenes and heavy components may continue to flocculate all the way throughout the system until the petroleum reaches its final destination. In this pilot project, asphaltene inhibitor and viscosity reducer agents were selected for reducing oil viscosity and breaking the tight emulsion phenomena in the selected piloting well in Um-Ghdair field. It was noticed that there is an asphaltene compounds flocculate in the interface between oil and water leading to increase crude oil viscosity. The best two among 22 chemical formulations tested through the screening process at lab scale and take it to pilot stage. Additionally, the pilot study examined the influences effective for surfactants such as water composition, temperature, concentration, pH and total dissolved solids. It was noticed that the viscosity reduction and the water separation improve with increasing surfactant concentration and increasing temperature up to 50 F. Two formulations were selected based on cost effective optimal concentrations of surfactant that identified from the bottle test. The pilot has been implemented successfully in the field, resulting a reduction in non-production time and increase the oil mobility from the reservoir.

Production forecasting always play an important role in decision-making for the corporate management and architecting company’s strategy. In an oil or gas upstream company, the yearly projection of future hydrocarbon production is a routine practice for preparing annual business plan. As sub surface uncertainties pose a huge threat for upstream company, achieving the forecasted crude oil and gas production is challenging in most cases. For national oil companies, having multiple Assets and thousands of wells where reservoirs underpinning different projects at different stages of field development, there arises a need of unified production forecast process that ensures forecast quality. In view of this, Kuwait Oil Company (KOC) initiated a process for improving forecast process adopting best reservoir management practices. In order to make reliable predictions, the engineer must be knoweledgable in variety of techniques to cover the wide range of conditions that are encountered in forecasting process. At the same time, engineer should have enough experience of reservoir behavior to alert the ways in which a given procedure might be changed to yield the most reasonable outcome. In case of using multiple methodology during preparation of forecast, the results obtained by each method and its underlying factor of judgment must be accounted.

The topic intend to share here is focused on developing a quality assurance process for base forecast in a mature reservoir. Base forecast, which also known as no further capital investment case refers to the production forecast that would result from existing wells and facilities. For national oil company’s having huge portfolio of brown reservoirs, base case wedge comprises a significant part of forecast volume. For the base forecast of brown reservoirs, it is always useful to use multiple methodology as applicable and compare their outcome for quality check. A combination plots from multiple methodology helps the field engineer to understand the underlying uncertainty in the forecast. Now a day it is common practice to generate forecast from numerical reservoir simulation model. In contrast, performance base methodology like decline curve analysis (DCA) for brown reservoir shows higher accuracy. However an effort should be made to obtain accurate rate and pressure data to improve the reliability of decline curve analysis (Reference- Fetkovich et al, 1987). A case study has been conducted for base case forecast on a reservoir from the state of Kuwait to identify practical issues and to develop a standard process. This paper is to share this study outcome as well as also to points out some of the uncertainties inherent in the production forecasting and outlines possible solutions. Field examples illustrating some of the challanges in estimating future production rates while using production performance analysis techniques are illustrated here. The need for careful scrutiny of all geological and engineering data is to be consider during forecast preperation. A wokflow has been developed for preparing base forecast and conduct decline curve analysis which lead to enhence reliablity of forecast.

This study presents the development of predictive models (for a single or a group of wells) to proactively manage water production in a heavy oil field in North Kuwait (average 16 °API crude, ~100 cp at 100 °F). Each resulting model is based on reservoir and time-dependent wellbore mechanical parameters that were obtained through several well tests. The primary goal is to be able to predict the water cut based on a well's current as well as "possible near-future" operating conditions.

The exercise involves using advanced predictive methodologies. Standard parameters (referred to as features) include water cut as the response variable along with pump intake pressure, total liquid production, pump speed, and surface unit power consumption among others as response variables. Random Forests^TM (RF), Alternating Conditional Expectations (ACE) and Multiple Linear Regression (MLR) algorithms were tested. Features were primarily grouped based on clustering. The predicted results are compared to history. The resulting models are further extended to generate type curves for future water cut prediction for different combinations of the operating parameters.

MLR and ACE based predictive models showed promising results. These algorithms also provided a way to characterize the influence of different features on the model outcome (i. e. predicting historical water production). The model quality significantly improves when only time-dependent features are used. The static parameters (e.g. permeability, perforation stand-off to oil-water contact, pump size) do not appear to have a big impact on improving the model score. Also, improvements were observed by introduction of principal component analysis (PCA) followed by clustering. The generated type curves allow the prediction of the near-future water production and to proactively adjust a well's operating parameters.

A predictive model is a time-saving tool compared to full range dynamic simulation. This provides a means to the engineers to establish an operating envelope for a well (or a group of wells) to manage water production. This is also a precursor to a full-scale machine learning process that could be implemented for automatically updating models when the quantity of data is expected to be massive in the coming years.