The Heterogeneity Index (HI) process was utilized in order to demonstrate production gain opportunities in a very short period of time, in a large mature ME field with around 500 wells producing from different reservoirs. The HI process provided a quick screening method of identifying preliminary candidate wells with anomalous behavior (over/under performance) for further analysis and most importantly, provided the foundation for the overall Structure Production Approach. This process; HI, can be calculated by utilizing OFM. A Cross Hair Plot has also been utilized to show the comparison of the HI of two variables in the same plot, creating an easy way to identify wells behaving differently from the average. The cross hair plot can be combined with X-Y Coordinated plot which reproduces the location of the wells. The results from this screening tool were utilized to identify the families of productivity problems at field level, and additional fast screening was done at well level to identify candidates for production enhancement. Representative Wells were selected for detailed diagnostics based on the relevance and size of productivity impact and, the potential of its production rate or well deliverability. Once a few "top potential" wells were identified, production engineering workflows were implemented in order to assess and forecast the potential of production incremental and try to determine and evaluate the best probable action. Some of the key innovate workflows used to complement production enhancement were: Time- lapse nodal analysis (honoring production history and neighboring wells), rate transient analysis (to consume sporadic/low frequency production data), single wellbore modeling (based on logs and flow units), among others. This paper will demonstrate the Production Enhancement Technologies Methodology, in particular the HI process with real examples; pending on data release approval from the owner and the progress of the operations.

Assessing the waterflood, monitoring the fluids front, and enhancing sweep with the uncertainty of multiple geological realisations, data quality, and measurement presents an ongoing challenge. Defining sweet spots and optimal candidate well locations in a well-developed large field presents an additional challenge for reservoir management. A case study is presented that highlights the approach to this cycle of time-lapse monitoring, acquisition, analysis and planning in delivery of an optimal field development strategy using multi-constrained optimisation combined with fast semi-analytical and numerical simulators. The multi-constrained optimiser is used in conjunction with different semi-analytical and simulation tools (streamlines, traditional simulators, and new high-powered simulation tools able to manage huge, multi-million-cell-field models) and rapidly predicts optimal well placement locations with inclusion of anti-collision in the presence of the reservoir uncertainties. The case study evaluates proposed field development strategies using the automated multivariable optimisation of well locations, trajectories, completion locations, and flow rates in the presence of existing wells and production history, geological parameters and reservoir engineering constraints, subsurface uncertainty, capex and opex costs, risk tolerance, and drilling sequence. This optimisation is fast and allows for quick evaluation of multiple strategies to decipher an optimal development plan. Optimisers are a key technology facilitating simulation workflows, since there is no ‘one-approach-fits-all’ when optimising oilfield development. Driven by different objective functions (net present value (NPV), return on investment (ROI), or production totals) the case study highlights the challenges, the best practices, and the advantages of an integrated approach in developing an optimal development plan for a brownfield.

A network modeling campaign for 15 surface gathering centers involving more than 1800 completion strings has helped to lay out different risks on the existing surface pipeline network facility and improved the screening of different business and action plans for the South East Kuwait (SEK) asset of Kuwait Oil Company. Well and network hydraulic models were created and calibrated to support engineers from field development, planning, and operations teams in evaluating the hydraulics of the production system for the identification of flow assurance problems and system optimization opportunities. Steady-state hydraulic models allowed the analysis of the integrated wells and surface network under multiple operational scenarios, providing an important input to improve the planning and decision-making process. The focus of this study was not only in obtaining an accurate representation of the physical dimension of well and surface network elements, but also in creating a tool that includes standard analytical workflows able to evaluate wells and surface network behavior, thus useful to provide insightful predictive capability and answering the business needs on maintaining oil production and controlling unwanted fluids such as water and gas. For this reason, the model needs to be flexible enough in covering different network operating conditions. With the hydraulic models, the evaluation and diagnosis of the asset for operational problems at well and network level will be faster and more effective, providing reliable solutions in the short- and long-terms. The hydraulic models enable engineers to investigate multiple scenarios to identify constraints and improve the operations performance and the planning process in SEK, with a focus on optimal operational parameters to establish effective wells drawdown, evaluation of artificial lifting requirements, optimal well segregation on gathering centers headers, identification of flow assurance problems and supporting production forecasts to ensure effective production management.

Successful matrix stimulation engineering depends on knowledge of formation damage and its extent in causing the well not to produce to its potential. Stimulation design is a complex procedure as formation properties need to be honored when designing for sandstones, whereas for carbonates reservoirs, the presence of secondary porosity along with matrix requires a somewhat different approach. Operating companies have positive expectations for matrix treatments as they see it as a cost effective, efficient and safe approach of intervention to restore or stimulate production of a well that has formation damage.In the oil and gas industry, diverse software applications are available to model matrix stimulation treatments but validity of such models depend on their robustness, starting with input data; i.e. formation characteristic such as permeability, porosity, pressures, temperature, skin-damage and its extent have strong impacts on the simulation results. Not very often is all of this information available; and if it exists, sometimes it's not up to date or questions may arise about the accuracy. This leads to operators to question how valid the design is and if it will allow them to meet their goals or not? This paper describes an innovative approach that has been implemented to establish an effective stimulation design and forecast results based on execution parameters. The idea consists in using real-time downhole pressure and temperature measurements, along with Distributed Temperature Sensing (DTS) acquired during matrix stimulation execution. The downhole data is utilized to calibrate the formation and fluid characteristics via pressure matching which provides ability to determine how much of actual skin is reduced. This result is then incorporated to a reservoir model to forecast and evaluate the potential of the post stimulation production results.

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