This paper highlights the efforts to mitigate the unprecedented situation of truncated gas demand via network modeling development and harmoniously harvesting value generation by its implementation. Global Pandemic in year 2020 resulted into unique situation of steep truncated gas demand due to economic slowdown worldwide. Hence as a prudent operator its deemed necessary to pursue strategic ideas and innovative concepts to manage offshore complex gas network to handle reduced supply demand balance, whilst protecting fulfill technical and contractual obligations also by optimizing value generation. It therefore demands the development and implementation of robust integrated system (end to end value chain system for hydrocarbon molecule) that would be leveraged on for agile response for deploying appropriate resolution considering dynamics of supply/demand balance and system equilibrium. This study focuses on a state of art that was commenced to develop an End to End Holistic Network Model from well head (fields) to product delivery terminal to scrutinize the complex offshore facilities to decipher appropriate pain points in terms of capabilities, risks, uncertainty, opportunity and exposures by performing robust analysis for trouble shooting, root cause analysis, gap analysis and expansion strategies for required scenario(s). A novel approach was influenced to create simulation model for complex network with building components i.e. source (100+ fields), sinks (multiple terminals), connectors (120+ export pipelines and ∼8 gas highways) along with pressure boosters (pump/compressor) etc. embedded in model. Major hubs, sub-hub, spill-over pipelines/loop lines including main gas transporting facilities with dedicated receiving terminal which formed integral part of network were also modeled in single platform. Flow co-relations for hydraulic estimations and material balance calculations along with engineering thermodynamics formulae for seamless data transfer in collaboration with operations were inbuilt for representative and resilient results. Simulation model was further validated with actual plant data as history matching and that precise forecasting analysis output. Multiple scenarios utilizing system ullage/ pipeline hydraulics (adhering to first principles) were studied and suitable alteration in operating philosophy e.g. were proposed to cater the truncated demand and to shape development strategies for future portfolios. Multi-level diagnostic was conducted to assure that system parameters such as operating pressure, velocity limits and required quality specifications are within operating envelope for the entire landscape. Lookahead analysis (what-if scenarios) were performed to evaluate to root cause analysis and troubleshoot at various intensities of the network to cater for equilibrium balance. Multiple contemplating scenarios were accomplished to analyze complex network parameters such as ullage opportunity, pressure variations, hydraulic fluxes, potential choking of low-pressure wells/fields & prospective blending specifications with variations in the supply/demand outlook. Gap analysis was executed in addition to arrive at necessary alterations for operating philosophy, partial segregation of system for pressure balancing due to low flow volume and product quality adherence. Model output assisted to gauge the potential for operating network by implementing appropriate reforms to optimize truncated flow in system and ensuring system is above its minimum turndown rate flow regime and could also propose to have the necessary mitigations to be in place for vigorous liquid management system due to low flow in network. Above methodology describes how by developing an end to end network model that summarize the granularity of a complex offshore network has facilitated to steer the agile response for operating envelope to cater the fluctuations in the demand/supply balance and optimize offshore allocations by network balancing for value maximization and to form a vision of portfolio strategy for future developments.

Gas turbines age and sometimes have problems that may lead to a trip or failure event. This paper describes how one can reliably forecast such problems at a future date so that the problem can be dealt with in the present. These methods are data-driven by relying on machine learning to develop the mathematical description for the gas turbine. First, all instrumentation is combined in a state of the turbine that is forecasted into the future using a long short-term memory (LSTM) network. Second, this forecast is analyzed by a neural network trained to recognize normal healthy behavior in order to identify problem. Third, problems can be diagnosed in some special circumstances by recognizing their fingerprint using past examples of the same failure mode; this is particularly relevant to vibration data.

Capillary pressure and relative permeability are essential measurements that are directly affecting multi-phase fluid flow in porous media. The difficulty of calculating them rises being constrained to core analysis in the laboratory with many challenges of mimicking reservoir conditions. This makes capillary pressure measurement process to be both time consuming and expensive. However, as resistivity is conveniently obtainable, it can be used to predict both capillary pressure and relative permeability given all relation to wetting phase saturation. Artificial intelligence methods have achieved promising results in modeling extremely complicated phenomena in oil and gas industry. This study aims to find a relation between all of resistivity, capillary pressure and relative permeability. Ultimately, we are going to generate a model by utilizing Artificial Neural Network (ANN) technique to predict both capillary pressure and relative permeability from resistivity. In addition, the implemented technique will be used to improve the data quality and to extend its resolution to thousands of data points. After that, as countless artificial neural network architectures can be created, the most efficient one will be evaluated given its performance and accuracy results. This paper presents the use of artificial neural network technique to both model and predict capillary pressure and relative permeability from resistivity attained from core analysis, and define core samples pore distribution systems. It was found artificial neural network architecture captures the complexity of the physics of the problem. Thus, it successfully fulfilled the required prediction objective. Additionally, compared to the traditional methods, this is proved to be accurate, fast, and significantly cost effective. Consequently, this process could replace the current traditional approaches. Finally, as artificial intelligence techniques are improving exponentially over time nowadays, this will increase the accuracy of the model predictability majorly.

Multilateral wells may vary in the number of laterals from two to even more than ten laterals. To control production on a lateral level, advance completion equipment, such as Inflow Control Valves (ICVs) and dual ported Permanent Down-hole Monitoring Systems (PDHMS) for real-time pressure measurements, are installed in the motherbore against each lateral to regulate reservoir fluids into the production tubing of the well from individual or collective set of laterals. Multilateral wells equipped with advance completion tools become complex wells (hereinafter called the "complex wells") in reservoir engineering. Recent complex wells involve multiple designs and architecture for which the reservoir exposure for laterals sums can be thousands of meters. A new generation of smart multilateral well completions are Manara type wells where the laterals are divided into a number of segments or compartments using oil swell packers and Manara stations are placed against each segment for quantifying liquid rate, water cut and real-time reservoir pressure measurements. Each station is equipped with electrical control valve for controlling unwanted fluid production at a segment level. In this work, a new workflow is established to model and history match Manara well with complex modeling features using the GigaPOWERS (GP) simulator and a compositional full field model. The established segment level history matching workflow includes four important milestones to achieve. The first is advance well completion design at which the physical well completion details are translated and converted into a grid based completion details with the help of pre-processing tools. The second step covers the generation of three complex well-related input files for the GP simulator. The third step is the process of validating high frequency performance data including flow rate, choke size and pressure for the complex well stations. The last step involves conducting the history matching exercise on a segment level for every individual station to achieve the final history match model. The conventional history match procedure includes generally three levels to match; field, group and well. In this workflow, station level match is done for the first time with a full field compositional model with a size of more than 61 million grid cells. The high definition history match is achieved at segment level for six stations that constitute a trilateral well with high frequency performance data. Complex well modeling in GP includes pressure drop calculations for several components. The pressure drops are related to friction, gravity, acceleration and advanced tools (i.e. ICV). Modeling a complex well involves several pre- and post-simulation environment features at the segment level that should work in complete consistency. The achieved outcome enables a business impact evaluation for an accurate value proposition for complex well incremental rates, cash flow streams and sensitivity prediction cases.

The Qaidam Basin is located in the northeastern part of the Qinghai-Tibet Plateau (or Tibetan Plateau). It is the world's highest altitude petroliferous basin (the altitude is usually more than 3000m above sea level). But this basin is more famous for its ‘double complex’ in terms of surface and subsurface structures. Our study areas, YingXi and YingZhong are located at the southwest of YingXiongling structural belt within the Qaidam basin. From the , we can see the "double complex" structures very clearly. The surface is full of wind erosion mountains . The serious elevation variation proposes huge challenge for seismic data acquisition, statics correction and near surface modeling. Meanwhile the subsurface is thrusted severely . The shallow part is thrusted with detachment faults and mainly comprised of severely deformed fold and Shizgou fault, which is the main fault of the survey. The horizon offset across it is usually quite large up to 2km. The study zone is more than 4000m deep where it is thrusted into imbricate fans with very strong overlying structural disturbing. The structure is cut by several groups of low angle faults. This kind of geological condition may produce quite good traps for oil and gas. Besides the overlying salt layers of the study zone extend to certain range which can be very good seal to prevent the oil and gas from migrating upward along Shizgou fault. These extremely complex geological situations cause a series of world-class E&P difficulties, such as how to obtain better seismic image, where to drill exploration wells, how to design and drill horizontal development wells with higher success rate, and how to submit OOIP or Reserve to the country for so complex region, etc. But to solve these difficulties, one thing is the key, i.e. the accurate structural delineation. This also the final goal of our work. In this paper we will discuss and show how we reach it by integrated Geophysical & Geological modeling.

A fit-for-purpose integrated subsurface study was carried out in a tight gas field in Oman to evaluate the stimulation process in horizontal wells. The objective is to explore the role of vertically and laterally heterogeneous in situ stress for hydraulic fracture (HF) propagation, and to quantify its effect on hydraulic fracture stimulation efficiency using a 3D fully-coupled hydraulic fracturing simulator for complex geological conditions. The use of advanced simulation tools that realistically predict the evolution of stresses in 3D allows us to explore the parameter space required to optimally design stimulations for complex tight and unconventional field development [Izadi et al. 2017; Cruz et al. 2018; Izadi et al. 2018]. The goal of this study is to test scenarios that increase production through reservoir contact by use of various fracturing techniques that improve the Stimulated Rock Volume (SRV). The modeling scenarios include an assessment of: The impact of high vs. low in situ stress on pressure response, proppant placement and near wellbore conductivity The impact of in situ stress magnitude in near wellbore conductivity The impact of fluid properties and landing zone on proppant transport The effect of laminations on proppant transport The impact of HF/natural fracture interactions on SRV This study illustrates that for complex reservoirs where spatial heterogeneities, preexisting natural fractures, or transitional stress states are present, advanced 3D modeling provides insight critical to optimize development strategy. Through parametric stimulation modeling design, mechanisms driving drilling, completion, stimulation and productions processes can be honed to optimize and better manage the primary risks to development in tight/unconventional reservoirs .

The process of fluid injection in oil reservoir to maintain reservoir pressure is a common practice to increase well productivity and enhance oil recovery. These activities cause a change of natural fractures and permeability in stress-sensitive and brittle rocks. Numerical modeling of the evolution in permeability and stiffness of near-wellbore region is required to capture the effect of coupling between fracture dynamics and poromechanics on fluid flow across the length scales of fractures and the host rock. We present such a numerical framework to model dynamic evolution in rock properties based on a coupled formulation of multiphase flow, rock deformation and fracture-induced damage processes. Our framework can handle complex fracture networks with randomly distributed fractures and can model both length and direction of propagating fractures to accurately capture permeability growth within these formations. A sequential solution is used to solve the three coupled problems: (1) multiphase flow, (2) mechanical deformation, and (3) fracture propagation. An implicit finite volume method is used to solve the multiphase flow problem using the Matlab Reservoir Simulation Toolbox (MRST). Following a one-way coupled scheme, multiphase pressures are used in the solution of the quasi-static mechanical deformation problem using the finite element method. This is followed by the solution of the fracture propagation problem and associated evolution in rock properties using the damage mechanics method. The continuum damage mechanics method allows us to use the change in the state of stress computed by the flow-mechanical solver to model the changes in permeability and elastic properties via the damage tensor. We test the capability and efficiency of our framework during simulation of different waterflood patterns in fractured reservoirs and discuss its usefulness in calculating optimum injection/production rates.

As a Brown Field, located in North Africa. Approximately 95% of Zarzaitine field wells are utilizing Gas Lift as an artificial lift method. The field has a challenging situation to optimize its Oil production; A detailed understanding of the production sys tem thermohydraulic, facility design and the amount of gas injection will ultimately have a major effect on production target. For this purpose, modeling the entire production system was necessary to properly account for the interdependency of wells and surface equipment and determine the system deliverability as a whole by optimizing Gas Lift injection. This paper presents an approach which was introduced for the first time in this field to ensure gas is used efficiently using a multiphase flow simulator for wells and pipelines to model the entire field Production Network in addition to the Oil producing wells including Gas lift mandrels. The model includes 112 Gas Lift wells with a detailed Gas Lift valves system currently on production, each one has been matched against the latest valid well test, Seven Separation Centers, Production gathering pipelines, Production gathering Center and Gas Lift Injection Center. The study has been executed in three major phases: Well Modeling & Calibration, Network Modeling and Gas Lift Optimization. Total Oil production rate has been defined as an objective function during the optimization phase where the total Injected Gas Lift rate for the entire network and for each individual well have been defined as varying parameters; By having a network model calibrated against field data representing the operational conditions of the asset, performing Gas Lift Optimization was the natural next step. Subsequently, by simulating the production system with different Gas Lift Optimization scenarios to maximize Oil production rate under specific surface facilities constraints using the Production Network Model, a better insight of how gas injection rate affects the total production and an understanding of whether a smarter allocation of the current available gas is possible in comparison to the different scenarios has been accomplished. As a result of this Optimization by applying some local and global constraints a 10% Oil production increase has been achieved. This practice has been shown to be successful as predictive technique in a variety of ways specially for such brown fields with more than 60 years of production history. As a next step, to properly manage the real potential of Brown fields, a full field Integrated Asset Model could be created to capture the interaction between the surface and the sub-surface. This model will account for the complex interactions between reservoir, wells and pipelines.

In unconventional reservoirs, not only do we have to concern ourselves with having a sufficient hydrocarbon volume to justify development (Resource Fairway), but also whether producibility characteristics, especially the ability to effectively fracture stimulate, are favorable (commercially). While volumetric characteristics tend to be regionally variable resulting in changes in hydrocarbon fluid types, reservoir pressures, thickness, porosity, saturation, etc., at the basin scale, producibility characteristics tend to be more locally variable resulting in changes in natural fracturing, subsurface stresses, permeability, effectiveness of fracture stimulation barriers, etc., at the field scale. A stochastic approach encompasses both these trend variations (regional and local) while assessing the commerciality of the play. This paper will explain the workflow involved in creating a probabilistic model using a commercial tool for engineers to understand the mechanics of the model, quality-check the input distributions, compare the model results to those from other sources (such as empirical, analytical, or numerical models). This workflow also will help in seamlessly modifying the input parameters to generate "what-if" scenarios and stress test the base case of development projects. Due to the capital-intensive nature of Unconventional projects, it is important to economically model a series of staged-investments. The purpose of the staging is to responsibly expose incremental capital and identify course corrections. Several factors and combination of inputs affect the commerciality of unconventional developments. These inputs will then be sampled using Monte Carlo simulation to generate a multitude of trials and their summary statistics. The recoverable resources, costs, and economics can also be exported for any given trial, and the ranges of input parameters sampled to generate a given trial can also be extracted Five critical parameters and their ranges were identified for developing a stochastic Before Tax (BTAX) economic output reflecting the full range of outcomes. Well type curves and select components of Capex and Opex were identified as the primary drivers. Associated products, such as condensate, NGLs, ethane, sulfur and water, were modeled as a function of the primary product using dependent ratios. Variability in cycle time was added to account for delays in implementation and acceleration as learnings are applied. This paper will present how technological integration between legacy in-house developed tools and stochastic analysis engines, as the centerpiece, can provide management the required level of visibility on project economics and evaluation ranges. With the considerable time spared in carrying out economic assessments due to this workflow, engineers are able to spend more time in developing case scenarios, run sensitivities, and analyze outcomes and results to make informed portfolio management decisions.

Several wells have been drilled and stimulated in a tight gas field in Middle East, however, very few have been economic. Many wells encountered difficulties pumping the required treatment and a number of horizontal wells did not produce as expected. A fit-for-purpose integrated subsurface model coupling geophysical, petrophysical, and geomechanical models with fully 3D hydraulic fracture simulation modeling was carried out to provide the operational path forward to overcome these challenges. This study is a showcase of the applicability of our integrated approach to simulate fluid flow and proppant placement within complex, naturally fractured reservoirs where the interaction between induced fractures and natural fractures dominates hydraulic fracture propagation [ Izadi et al. 2015 ; Cruz et al. 2016 ; Izadi et al. 2017 ; Izadi et al. 2018 ; Cruz et al. 2018 ]. Seismic data calibrated with rock physics analyses were used to delineate structural components and incorporate rock properties into the high-fidelity 3D subsurface model. High porosity zones and areas of high natural fracture intensity were identified with seismic acoustic impedance and geometric attributes. Reservoir properties of porosity, permeability and gas saturations vary across this field. Variations in geomechanical parameters correlate spatially with variations in porosity and permeability. This paper demonstrates a methodology to evaluate reservoir heterogeneities on hydraulic fractures propagation through fully 3D simulations at a planned well's location.

A methodology to construct deep neural network- (DNN) and recurrent neural network- (RNN) based proxy flow models is presented; these can reduce computational time of the flow simulation runs in the routine reservoir engineering workflows, such as history matching or optimization. A comparison of these two techniques shows that the DNN model generates predictions more quickly, but the RNN model provides better quality. In addition, RNN-based proxy flow models can make predictions for times after those included in the training data set. Both approaches can reduce computational time by a factor of up to 100 in comparison to the full-physics flow simulator. An example of the proxy flow model application is successfully demonstrated in an exhaustive search history matching exercise. All developments are shown on a synthesized Brugge petroleum reservoir.

Over the years, considerable research has been conducted and the results of numerous studies have been published about optimizing drilling operations that maximize the footage drilled and minimize drilling costs. One of the optimization aspects studied is determining which drilling parameters improve drilling efficiency. Many drilling application software packages exist to help engineers simulate the drilling process. These software packages might include the capability to evaluate drilling parameters’ sensitivity; however, many of the software programs only focus on a specific engineering area and rely on the engineer to evaluate the relation between the results of an engineering analysis and deciding. A new approach has been developed that consolidates various engineering analyses into a single workflow to automatically define the optimum drilling parameters. The application provides a drilling parameters roadmap consisting of weight on bit (WOB), surface rpm, and flow rate for efficient drilling execution. This application uses the knowledge of offset wells as a starting point to define the WOB and RPM ranges. Those values are represented as a drilling parameter matrix for input into the model. Then, by using static and dynamic bottomhole assembly (BHA) modeling and hydraulics modeling in addition to predicted rate of penetration (ROP), the application runs various sensitivity analyses to obtain the optimum drilling parameters within rig and downhole equipment specifications. The sensitivity analysis with static BHA modeling provides the buckling check to the maximum WOB proposed while the dynamic BHA modeling provides downhole vibration analysis and predicts the drilling ROP, which is compared with downhole tool specifications. Then, the hydraulic modeling provides validation of hole-cleaning quality for the relation between flow rate and ROP, without exceeding rig pump and circulating system pressure specifications. The application also implements a smart learning process to reduce the number of iterations and computation time. The results of this approach were tested on a field project where implementing the roadmap computed from the application showed excellent results for improving drilling performance.

The Albert Basin of Uganda is located at the northern end of the western branch of the East African Rift System. It is a graben rich in oil and gas with a shallow research degree. In the south of the basin, a fan delta system controlled by the boundary fault is developed in the Miocene formation. Due to the few wells and poor quality of seismic data in this area, it is difficult to predict the spatial distribution of sedimentary reservoir sands. In this paper, sedimentary forward modeling coupled with 3D geological modeling is used to provide new ideas for reservoir prediction. Sedimentary facies analysis is based on core description, well logs, paleontology, heavy mineral content and grain size data. Quantitative analysis of accommodation space, source supply, and sediment transport parameters can help explain the main factors that controlled the sedimentation. Milankovitch cycle method was used to establish the time scale of the basin. The simulation results were combined with 3D geological modeling to quantify the characteristics of the sand body distributions. Sedimentary facies analysis shows that the Miocene formation in the south of Albert Basin deposited in a shallow lacustrine environment. A proximal fan delta deposition with subaqueous distributary channels was controlled by the east boundary faults. Firstly, the accommodation space was estimated according to the thickness of the stratum and the change of the ancient water depth. The source supply was estimated by the area of the project and formation thickness, and the transportation parameters were estimated according to the nonlinear transportation model based on the traction flow with a little gravity flow. Secondly, an astronomical stratigraphic framework of the Miocene strata in the south of Albert basin was established through the Milankovitch cycle stratigraphy, and it was used to restrain the process of stratigraphic forward modeling and to reproduce the sedimentary evolution process in the geological historical period. Thirdly, the stratigraphic forward modeling results were resampled into the geological model, a 3D reservoir probability distribution model is established from trend modeling to quantitatively characterize the spatial distribution of sand bodies. Finally, the sandstone distribution simulation results were transformed into quantitative control constraints for 3D geological facies modeling. Thus, the new approach significantly promotes the facies model quality and provides robust results for petrophysical property models. Integration of stratigraphic forward modeling with 3D geological modeling can effectively solve the problem of reservoir characterization in an early stage of oilfield development through the interaction of the dual model coupling. This method has unique advantages in the reservoir research in the area with fewer data and great variation of sand.

In hydraulic fracture stimulation of conventional reservoirs (e.g. tight gas and deepwater unconsolidated sands), the use of sophisticated design models is almost indispensable. Typically, each hydraulic fracture is sequentially pumped and executed from near vertical wellbores. It is well understood that the optimum post-fracture well productivity is directly linked to hydraulic fracture dimensionless conductivity, which is governed largely by propped fracture length and width. From an engineering and operation execution point-of-view, the goal is to pump into the fracture and pack it with the desired volume of proppant without encountering premature ‘screen-out’. Therefore, the prediction of fracture geometry, the selection of ‘pad’ volume and proppant pumping schedule become critical for propped fracture design. In practice, a wide range of models have been deployed successfully. However, such considerations do not appear to be important for unconventional resources where multiple fractures are pumped from a long horizontal well. In fact, multi-frac horizontal well technology has advanced through field trials and experimentation, often without much help from modeling or understanding of multiple-fracture mechanics. This begs the questions of what areas of research and model design parameters should we focus on? Can we avoid the details while dealing with the big picture such as fracture spacing, horizontal well length/direction, the well's landing depth, and their impact on cost and production? This extended abstract revisits the motivation and well-accepted design approach for hydraulic fracturing in conventional reservoirs and some of the challenges facing hydraulic fracturing design in unconventional reservoirs. Practical first-pass design considerations based on geomechanics for fracture spacing and horizontal well landing depth selection are outlined.

Pangkah field located at offshore East Java Indonesia is an oil and gas producer within fractured carbonate reservoir lithology. Fractured corridors introduce challenges during drilling and production phase. To optimize production, several wells had been completed with novel Inflow Control Device (ICD) design. This paper highlights the potentials of such customized ICD's innovation with integral sliding sleeve. This novel ICD design preserves intended ICD position, and in addition could be shifted in the downhole later either to be in open or closed position if required. Either coil tubing or electric-line could be deployed to shift the downhole 3-positions of this integral ICD with sliding sleeve. The integral design allows a fully open position for well stimulation or circulation in formation damage treatment. Meanwhile another position enables ICD configuration for inflow control. The last position which is fully-shut or closed can be activated during completions string Run in Hole (RIH) to allow floating toward Total Depth (TD), minimizing torque and drag, and maintaining wash-down capability without wash pipe. The whole closed or floating system while RIH makes it possible to set assemblies of open hole hydraulic set packers or any pressure activated devices. During late-life, selective compartment could be shut-off against excessive water production decisively whenever required. Good sealing of the open hole packers against borehole is essential for the effectiveness of open hole compartmentalization and ICD production. The open hole hydraulic packers must be placed and set properly within its expanding range. Combination of petrophysical analysis, caliper log, image log, seismic coherency, and drilling loss data are used to define the compartments and packer placement. This novel ICD's innovation with water shut-off capability in addition to the main ICD's inflow control is designed comprehensively with 3D single wellbore dynamic modeling. ICD's single well dynamic modeling were executed along three phases i.e. pre-drilled, real-time and post-job. Each phase respond to operation readiness and reservoir management. Intensive engagement with asset team and realistic modeling design range lead to positive production results.

Significant challenges meeting together make Keshen gas field in Kucha foreland basin become unique from geosciences, engineering and economics points of view. These challenges generally link to harsh geography, super deep (>6500m TVD), thick conglomerates (up to 3000m), heterogeneous salt-gypsum laminations (up to 2000m), complex thrust-nappe structure, HTHP, and ultra-tight (matrix permeability < 0.1 md). This paper gives a comprehensive review how the geoengineering Long March assists to successfully develop this field. A geoengineering team was established to persistently attack on this world-class championship with high-level planning since 2012. Specific research and development of engineering technologies and solutions for data acquisition, drilling, completion, stimulation, testing and production and studies were taking place in parallel. To ensure seamless integration from geosciences and engineering to operation, a five-year geoengineering study was proactively and progressively executed which includes four major steps with respective objectives including 1) understanding fluid distribution and producibility, 2) well production breakthrough and enhancement, 3) optimization of well stimulation and economics, and 4) optimization of field management including surprising sanding problem. It was recognized three elements and their interactions are critical for production enhancement which are natural fracture (NF) characteristics, production controlling mechanism, and stimulation optimization under super deep, HPHT and extremely high stress conditions. The bottleneck for study was poor seismic quality due to super depth, pre-salt, and complex thrust-nappe structures. Hence the team established comprehensive methodologies with iterative improvements to overcome this bottleneck. Using regional structural geology, outcrops, cores, images and logs as inputs, structure restoration and geomechanics simulators were combined to perform structure restoration, paleo-stresses, and in-situ stresses and eventually 3D NF prediction. To understand production mechanism, analysis of geological and geomechanical factors, NF and stress relationships, single parameter and multiple variables, and transient and production performance were integrated. Big core studies were conducted to understand fracability, NF and hydraulic fracture (HF) interactions, and selections of HF fluids. Based upon, a stimulation optimization approach was implemented which included engineered completion designs, HF modeling and parametric studies, post-frac analysis and optimization, and time effects through high-resolution coupled geomechanics and reservoir simulation. All efforts with evolving knowledge were eventually developed as an interactive expert system to guide systematic stimulation optimization, sanding management and development optimization. With increasing understanding of reservoir, and implementing innovative solutions, it was enabled to drill wells at optimal locations with less time, simplified well configuration, and less constraints on stimulation and production operations. By 2017, well construction time was reduced by half, natural productivity of wells was doubled, productivity after stimulation was tripled, and overall cost of wells was largely reduced. The success achieved would boost confidence and lighten on development of other challenging fields.

Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics. Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences. In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed "Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'. The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model. The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.

Modeling and simulation play a key role in well construction planning, which can help to evaluate and optimize the engineering designs for a well. Today, many simulations use finite element analysis (FEA) and computational fluid dynamics (CFD) to model complex dynamic downhole conditions and behaviors of drilling tools. However, one challenge is that the complex simulation may take a few hours to run, which limits the usage to only a few well planning jobs. This limitation also poses as barrier in real-time monitoring applications, where under one second computation speed is required. In this paper, two approaches are presented for improving the performance of drilling simulations: smart depth selection logic for BHA tendency calculation, and reduced order model using machine learning for motor optimization modeling.

JZS oilfield is an offshore metamorphic rock fractured buried hill oilfield. It was put into development in July 2010. The overall production situation of the oilfield is good, but some problems have been exposed. The main performance is as follows: It is difficult to accurately characterize the heterogeneity of fracture space distribution; In the numerical simulation of fractured reservoir, it is impossible to accurately describe and predict the fracture flow of fluid channeling in corner point grid system. In order to solve the above problems, this study presents a new integrated fractured reservoir geological modeling and numerical simulation research method based on unstructured grid. There are three key aspects to this method. (1) The multi-scale (large, middle and small) discrete fracture system is established by combining outcrop measurement data with well point information and seismic attributes. On the basis of post-stack 3D seismic data, ants attributes are extracted, then the ant body results are transformed into large scale fractures; Using azimuth anisotropy attribute based on pre-stack inversion and combining the distribution orientation of large-scale fractures, the middle-scale fractures are established; According to the power law distribution relation between the cumulative frequency and the fracture length of large scale and small scale which based on outcrop observation, the imaging logging data and pre-stack inversion azimuth anisotropy attribute, small scale fractures are constructed by DFN technology.(2) For multi-scale fractures, the unstructured grid division technique is used to build a 3D model that conforms to the heterogeneity of dual media. In this study, a layered triangular prism grid generation technique is proposed. It is used to establish model of multi-scale fractures based on unstructured grid. Using large-scale fractures as a constraint, full 3D unstructured grid model is set up, and the discrete fracture model can accurately describe the fracture system and the coupling relationship between matrix and the fracture;(3)The triple-medium numerical simulation of the reservoir in the study area is carried out by using the automatic history fitting technology of ensemble kalman filter (EnKF). After several parameter adjustments, both the coincidence rate of the index and the fitting precision are higher than before. Multi-scale discrete fracture model based on the large-scale fractures discretization processing, equivalent medium processing to middle and small scale fractures, keeps the seepage characteristic of the large-scale discrete fractures model and ensures the calculation efficiency. The results show that the new method has obvious advantages in computing speed and that the fitting effect is closer to the actual production performance.

Drilling horizontal wells in YB gas field in Ordos Basin presents significant challenges due to severe wellbore instabilities problems in drilling through Permian Lower Shihezi and Upper Shanxi formations, where laminated shales overlies with sand and coal seam. In first phase of horizontal wells drilling, most wells encountered severe wellbore instabilities including pack-off, stuck-pipe, over-pull, drilling pipe lost in hole and even side track. Post-well analysis showed that these horizontal wells instabilities mainly occurred in Permian Lower Shihezi and Upper Shanxi formation where most cavings and drilling events (stuck-pipe, over-pull) were observed. In contrast, vertical exploration wells have no such instability issues in same interval. To analyze and understand the mechanism of wellbore instability issue and provide optimal mud weight and better drilling practice to reduce the risk of wellbore instabilities, an anisotropic wellbore stability modeling using Plane-of-Weakness (PoW) failure criterion was carried out in this study. The PoW failure criterion is adopted to compute the onset of rock shear sliding and/or fracture along a weak plane (bedding or fracture) and identify the potential wellbore instability risk in drilling through anisotropic rock formations. The influence of bedding orientation, rock anisotropic elastic and strength properties, and wellbore trajectory on the wellbore stability are all included in the model. This paper describes the process and workflow of conducting PoW wellbore stability modeling for YB field wellbore drilling. The proposed drilling parameters (stable mud weight) from the modeling and its application and improvement for next wells drilling, are also included. The analysis showed that the laminated shale and coal intervals were very prone to fail when well drilled with deviation between 60 0 to 85 0 . The stable mud weight computed from PoW for drilling through these intervals is 1.40-1.45 g/cc, where as it is 1.20-1.25 g/cc from conventional isotropy wellbore stability model, which was not enough to keep wellbore stable. Based on results from PoW modeling, drilling mud weight scheme was updated and applied to another 3 horizontal wells planned at nearby location. All these three wells were drilled and completed safely without severe wellbore instability issue. In these wells’ 216mm (8.5 in) section, wellbore instability related non-productive time (NPT) was reduced about 11.5 days per well and section time was reduced about 26 days per well. This PoW modeling was first time applied in wellbore stability analysis for horizontal well drilling at Ordos Basin and the results are satisfied and encouraged. The insights provided in this paper suggests that, for drilling in other locations with similar instability challenges, PoW modeling will be a better choice to provide solution and recommendation to ensure drilling safely, improve drilling efficiency and reduce drilling costs.