Cumulative Oil Production Research Articles

Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial reservoir simulators are able to predict production curves with high confidence, but repetitive tasks in a few cases may spend a precious time of staff as well as require a large computational effort. Although artificial intelligence (AI) is providing an alternative path to the usual workflow, many commercial simulators lack robust AI algorithms. This work introduces a methodology based on a multilayer perceptron (MLP) neural network to predict the final cumulative oil production of a reservoir at vertical wells that cross hydraulic flow units (HFUs), which are volumes endowed with good flow attributes. Each well location is attached to special spots previously determined from clustering and calculation of maximum closeness centrality points (MaxCs) within a class of HFUs. The database is divided into training, validation, and testing sets organized after processing the UNISIM-I-D synthetic model, representative of the Namorado Field, Campos Basin, Brazil. The key rationale of this paper is to use the feature of MaxCs of being drivers for well placement as knowledge base to learn the production mechanisms of the oilfield. The outcomes are presented from two perspectives: an original MLP and its post-processed version. Both are compared with reservoir simulations carried out in CMG Imex^{copyright } and achieve reasonable agreement. The performance is measured by root-mean-squared error (RMSE) and mean absolute scaled error (MASE) both in original and post-processed versions. We show that average RMSE and MASE values near 0.07 and 14.00, respectively, are achieved without post-processing. With post-processing, gains of up to 43% are reported for the integral oil volume.

Dynamic reservoir sand characterization of an oil field in the Niger Delta from seismic and well log data

Many oil fields within the Niger Delta have been abandoned after serving their estimated life time largely due to large uncertainties and risks associated with many subsurface complexities and often limited and/or inconsistent surveillance data coupled with the convectional static characterization of the reservoir bodies as is the case of the X- oil Field, south eastern onshore, Nigeria. This research presents a pragmatic approach of integrated dynamic reservoir modelling with uncertainty management techniques, which resulted in robust productivity of the abandoned oil field. The objective is to run and improve comprehensive sensitivity analysis of petrophysical properties in the reservoir, develop PVT analysis, and predict acceptable original oil in place (OOIP), comprehensive production history and expected cumulative oil recovery factor (RF). Integrated dynamic reservoir modelling of static interpreted reservoirs of two vertically stacked reservoirs (A and B) was done. The analysis identified reservoir A with an OWC at 5520 ft with thickness of about 300 ft and STOIIP of 59 MMSTB has an underlying reservoir B with OWC at 6100 ft with thickness of about 300 ft and STOIIP of 21 MMSTB. Average permeabilities and porosities of both reservoirs range from 2.54 to 1115.2 md and 0.2 to 0.33 respectively, with estimated cumulative oil production of 46.62 MMSTB at 55% RF in reservoir A and 17.21 MMSTB at 48% RF in reservoir B. This was estimated to correspond to maximum cumulative oil production of 40 MMSTB by August 2020 indicating that the overall recovery from the field could potentially be increase by 6% from drilling infill wells with drainage targets. Production data analysis indicates that the recovery factors and well recoverable volumes are highly correlated to average net oil pay. The correlations may be used for reserve quality control. It is recommended that improved quality of data couple with integration of statics with dynamic modelling should be a priority to reduce uncertainties and solve problems in situ in field operations leading to increase in oil recovery during activation of abandoned oil field.

Impact of physical properties on material balance calculations: case study AL-Nasr oil field, Shabwah Governorate

The accuracy of many petroleum engineering calculations (e.g., material balance calculations, reserve estimation, well test analysis, advanced production data analysis, nodal analysis, surface network modeling, surface separation, and numerical reservoir simulations) largely depends on the accuracy of the data on pressure, volume, and temperature (PVT). An approach to improve the material balance calculation method is presented in this study. To achieve the objective, PVTP and MBAL Software programs were applied. PVT model was built by different methods. Software was implemented to calculate stock-tank oil, initially in place (STOIIP), using different approaches. Sensitive analysis was performed to investigate the effect of different parameters on material balance calculation. Illustration of the proposed approach was done on the data obtained from AL-Nasr oil field. The Average absolute error of PVT properties such as bubble-point pressure, relative volume, oil formation volume factor, Solution gas oil ratio and oil density is used to compare between actual and calculated values. Results showed a good agreement. History production (cumulative oil production, cumulative gas production, cumulative water production) were also calculated to investigate the match between history parameters and calculated parameters. Results indicated that the impact of PVT errors on material balance calculations can be significant.

Application of Surfactant Solution for Lower Oligocene Formation, White Tiger Field

Summary In this study we investigated the possibility of applying a surfactant solution for better oil recovery of the Lower Oligocene formation in the White Tiger Field. The research comprised steps ranging from optimizing the surfactant solution to coreflooding and piloting on two injection wells. The concentrations of surfactants [alkyl ether sulfonate (AES) and sodium α-olefin sulfonate (AOS)] and solvent (n-butanol) were optimized using response surface methodology with the response value set equal to the interfacial tension (IFT). The certified solution was used for conducting coreflooding, and the additional recovery factors from two sample cores were 32% original oil in place (OOIP) and 49% OOIP, respectively. Two injection wells were selected for piloting the injection and were located in White Tiger Field. These wells exhibited strong hydraulic connectivity to seven wells that were under production. The production rates in the observed wells demonstrated a significant increase after 2–3 months of surfactant injection. During the observation period, all seven producer wells showed an increment of approximately 8,475 tons of oil, accounting for approximately 21% of the cumulative oil production.

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process.

The purpose of this study is to develop a data-driven proxy model for forecasting of cumulative oil (Cum-oil) production during the steam-assisted gravity drainage process. During the model building process, an artificial neural network (ANN) is used to offer a complementary and computationally efficient tool for the physics-driven model, and the von Bertalanffy performance indicator is used to bridge the physics-driven model with the ANN. After that, the accuracy of the model is validated by blind-testing cases. Average absolute percentage error of related parameters of the performance indicator in the testing data set is 0.77%, and the error of Cum-oil production after 20 years is 0.52%. The results illustrate that the integration of performance indicator and ANN makes it possible to solve time series problems in an efficient way. Besides, the data-driven proxy model could be applied to fast parametric studies, quick uncertainty analysis with the Monte Carlo method, and average daily oil production prediction. The findings of this study could help for better understanding of combination of physics-driven model and data-driven model and illustrate the potential for application of the data-driven proxy model to help reservoir engineers, making better use of this significant thermal recovery technology for oil sands or heavy oil reservoirs.

A practical integrated forecast method for estimated ultimate recovery (EUR) and well production performance after water breakthrough during waterflooding in naturally fractured reservoirs (NFRs)

It is both an opportunity and a challenge to develop naturally fractured reservoirs (NFRs) effectively and efficiently, especially with waterflooding development measures. In order to make production performance analysis and estimated ultimate recovery (EUR) forecast for NFRs during waterflooding easier and more widely applicable, a practical integrated method was established in this study based on two-phase flow characteristics of oil and water in NFRs and classic reservoir engineering theories of non-piston like displacement of oil by water, which consists of several novel practical forecast models for EUR and well production performance. Firstly, the well-controlled original oil in place (OOIP) of an NFR can be quickly fitted and obtained from actual production data by the established relationship between cumulative oil production and cumulative water production. Then, the maximum cumulative oil production, namely, the EUR of the production well can be determined when water cut is infinitely close to 1, and the well production performance in the future can be conveniently forecasted by the derived generalized relationships between water cut and other production characteristic parameters including cumulative water (or oil) production and recovery factor of well-controlled OOIP (or EUR). A lab-scale and two field-scale NFR models with different well patterns were established and simulated so as to verify whether the practical forecast models proposed in this paper are reliable and accurate, and comparison results indicated that all these models are in good agreement with the simulated production data. Sensitivity analysis for the curve fitting model of well-controlled OOIP demonstrated that viscosity ratio of water phase to oil phase has an enormous influence on the model and residual oil saturation also has a greater influence on the model compared with connate water saturation. In addition, two actual production wells in the X oilfield were selected as case wells for field applications and it was found that the unified type-curve for recovery factor performance forecast can be determined and employed for different production wells in the same oilfield unless the viscosity ratio of water phase to oil phase, the connate water saturation and the residual oil saturation do not change comparatively. The authors believe that the practical integrated forecast method established in this study is of great help for field reservoir engineers to analyze production data and forecast EUR and well production performance in NFRs during waterflooding development processes more quickly and conveniently. • A practical model for EUR forecast during waterflooding in NFRs was proposed. • Well-controlled OOIP can be conveniently fitted by actual field production data. • Well production performance can be forecasted by the derived integrated models. • The novel method is of great help for field engineers to quickly perform PDA.

Sequential short-term optimization of gas lift using linear programming: A case study of a mature oil field in Russia

The present study examines the sequential optimization of total lifting-gas allocation into multiple producing wells via linear programming for the maximization of oil production in a mature field located in Russia. Because the injection rate of lifting gas depends on the well production status, the ten-year gas lift schedule is determined by performing the gas lift optimization on a sequential, bi-weekly basis. For each short-term production optimization, the linear programming is solved by considering four operating parameters (e.g., tubing head pressure, bottom hole pressure, oil production rate, and gas lifting rate) which are calculated via high-fidelity reservoir simulation runs. Compared to the constant rate injection case, the optimal injection case increases the cumulative oil production by 14% at the same expenditure. These numerical results indicate that the sequential short-term optimization of the gas lift can be a practical approach towards boosting the asset value of a mature oil field with consideration for the well production status.

Formation of hoodoo-upland on Ordovician karst slope and its significance in petroleum geology in Tahe area, Tarim Basin, NW China

Based on a large number of geological and geophysical data, the formation, fracture-caves types and hydrocarbon distribution of hoodoo-upland on the Ordovician karst slope in the Tahe area, Tarim Basin, are discussed by analyzing faults and strata thickness. The hoodoo-upland was made of high peaks and narrow valleys in the Ordovician karst slope during the Early Hercynian karst period, which were distributed along the NNE positive flower structure and had inherited evolution. The fault-fractures and fracture-vugs complex were extremely developed, with a thickness of 100 m. The cumulative oil production of 60% oil wells was more than 20×104 t per well in the hoodoo-upland, where the residual thickness of the Ordovician Yingshan Formation was greater than karst depressions. Caves formed by the shelter of collapsed breccias were developed in the valleys. They were 1.6 to 13.5 m high, with a filling rate of 51.6%. The positive flower structure under the settings of strike-slip compression controlled the early formation of the hoodoo-upland on the karst slope, resulting in the differences of drainage distribution and karstification. Compared with the water-rich karst valley, the hoodoo-upland with lean water suffered weaker karstification, had thicker residual stratum, and was higher in terrain. In rainy season, the meteoric water flew and corrode along the cracks, forming a complex network of fractures and caves. Combined with inherited uplift and the effective match of the NNE deep faults, oil and gas continuously charged into the reservoir space in the upland, forming the hoodoo fracture-cave reservoir with vertically quasi continuous distribution, high hydrocarbon abundance and high production.

Semi-analytical model to predict the performance of cyclic steam stimulation oil wells

Prediction of the performance of oil wells under Cyclic Steam Stimulation (CSS) is challenging in complex and heterogeneous reservoirs, especially with limited data. Analytical and numerical simulation models do not usually give accurate predictions in such conditions. In this work, a semi-analytical model was developed to determine consistent mathematical relationships between the injected steam and some of the effective oil production parameters for more accurate prediction of oil production rates. Field investigation indicates that the change of the Cumulative Oil to Steam Ratio (COSR) to production days is related to a group of effective oil production parameters. This group of parameters includes the cumulative injected steam relative to the drainage volume, the oil net pay thickness relative to the gross pay thickness, and the vertical permeability relative to the thermal diffusivity. These parameters were arranged in two dimensionless groups. It was found that plotting these two dimensionless groups on Log–Log scale for any reservoir yields a straight line (correlation). For any reservoir under CSS, measurements of two steam cycles are sufficient to identify the constants of the proposed correlation. This method has been applied and validated on six reservoirs with different reservoir characterizations. Six different wells with a total of 43 steam cycles from these reservoirs were analyzed with the same approach. The mathematical relationships of the dimensionless groups were calculated, and the Log–Log plot was constructed for each well using the data of the first two cycles. Then, the proposed correlation was developed for each well and used to predict the well performance starting from the third steam cycle. At the end, the predicted performance of each well was compared with the corresponding actual measurements. The results showed that the average absolute percentage deviation between the actual and the predicted cumulative oil production through the well lifetime is less than 5% for the six wells. In addition, the absolute instantaneous deviation between the actual and the predicted cumulative oil production for each individual cycle in all cases is (1) less than 15% for about 42% of the tested CSS cycles, (2) between 15 to 25% for about 39% of the tested CSS cycles, and (3) higher than 25% for about 19% of the tested CSS cycles. This work is considered an original contribution to develop dimensionless relationships that can be used to predict the oil production of the CSS operations for reservoirs with limited data. The required data are the historical production rate, steam injection rate, and basic petrophysical parameters.

A New Approach to Embed Complex Fracture Network in Tight Oil Reservoir and Well Productivity Analysis

Accurate modeling of the distribution of induced fractures and pre-existing natural ones in unconventional reservoirs is essential for the analysis of the productivity of horizontal wells after fracturing. A novel approach is proposed here to establish a three-dimensional geological model of induced fractures, natural fractures and the horizontal well using a discrete random distribution method, based on micro-seismic data and natural fracture properties from the S oilfield. Taking the stress sensitivity into consideration, a coupled model calculating the fluid flow in the horizontal wellbore, the reservoir matrix and fractures were established to simulate the productivity of the S oilfield. This model was calibrated by the production data in the S oilfield, and the productivity of horizontal well under different natural fracture intensities, hydraulic fracture spacing and lengths was analyzed. The results show that the area without stimulation in the tight oil reservoir can rarely be exploited. The fluid pressure in the horizontal wellbore cannot be treated as uniform, especially when the production pressure keeps decreasing; otherwise, the cumulative production will be larger than the actual one. Increasing the hydraulic fracture length and natural fracture intensity and decreasing the fracture spacing are all beneficial to the daily and cumulative oil production. However, these methods accelerate the decline of the daily production. The production decline rate with higher natural fracture intensity was about 4 times that of lower natural fracture intensity. The natural fracture intensity and the fracture spacing are factors that are more influential to the productivity compared to the fracture length.

A novel fluid–solid coupling model for the oil–water flow in the natural fractured reservoirs

The mutual coupling effect between the fluid flow and the in situ stress fields cannot be ignored during the development of natural fractured reservoirs (NFRs), such as in the waterflooding process. In this study, a discrete fracture model is proposed to simulate the rock deformation and two-phase flow behaviors of oil and water in the NFR. The numerical solution of the model is achieved via the finite-element method and control-volume finite-element method. The numerical simulator is verified using commercial software, and a perfect agreement is obtained. Finally, sensitivity analysis is conducted on the key parameters in the model, such as fracture parameters, matrix permeability, and injection intensity. Results show that the fluid–solid coupling effect gradually weakens with production time. The degree of the fluid–solid coupling on cumulative oil production becomes smaller as the permeability of the matrix increases. Fracture connectivity controls the velocity and direction of the water flood front. Water injection intensity directly affects the natural fracture opening deformation and well productivity. The research and the numerical results obtained in this paper can provide theoretical guidance for the optimal design of water flooding operations in NFR.

Nanoemulsion flooding for enhanced oil recovery: Theoretical concepts, numerical simulation and history match

In current scenario of depleting oil reserves, chemical flooding is a vital enhanced oil recovery (EOR) technique. Present study discusses laboratory flooding experiments of surfactant/nanoparticle nanoemulsions on sand-pack system, with bulk approach to model and simulate the flooding strategies portraying the potential of nanoemulsion assisted EOR. Physicochemical properties of nanoemulsions were characterized in terms of droplets size, interfacial tension, wetting behavior, viscosity improvement and crude oil displacement. A numerical methodological simulation is performed on porous media through CMG-STARS simulator. Cartesian single porosity model equipped with well pattern, injection fluid flow-rate and rock-fluid properties was developed via simulation tool during compositional investigation. Tuning of relative permeability values to successfully fit with cumulative oil production and water cut was extremely challenging role during model development. Oil saturation profiles and relative permeability curves displayed the alteration in wetting behavior of sand-pack system to strongly water-wet from oil/intermediate-wet condition. 23–28% tertiary recoveries were achieved, contingent to composition of surfactant/nanoparticle in injected nanoemulsion. History match (HM) of production data via CMOST for experimental and simulation outcomes exhibited a good match with global HM error < 6%. Numerical simulation investigation facilitates a holistic approach to predict the performance and practicability of nanoemulsions as chemical EOR fluid.

BHPSO combined with statistical net hydrocarbon thickness map for well placement optimization under uncertainty

Investments in oil and gas projects are driven by critical field development decisions including well placement, which often significantly affect the projects’ economics. Due to their typically high cost and inherently insufficient data (especially in greenfields, or fields in their early stage of development), managing uncertainty is critical when optimizing a field development plan. The use of a single deterministic base case for hydrocarbon, both in place assessment and production forecasting is often misleading and leads to sub-optimal decisions. Consequently, robust field development plans require multiple geological realizations covering the range of uncertainty in reservoir properties and encompassing both multiple geological concepts and geostatistical properties distribution. Typically, an objective function such as the average net present value (NPV) or the average cumulative oil production (COP) is optimized in order to select an optimal development scenario. Nevertheless, such an assessment can be computationally prohibitive, especially when using optimization methods require hundreds, often thousands of costly simulations over a single realization, a number that significantly increases when multiple realizations are involved. This study proposes a new method for well placement optimization under uncertainty, building on map-based evolutionary optimization technique: the black hole particle swarm optimization (BHPSO). The statistical net hydrocarbon thickness (SNHCT) map is introduced to guide the BHPSO algorithm; and hence, pragmatically account for uncertainty in the process of well placement optimization. We optimize well placement on the realization corresponding to the minimum difference between its NHCT map and the SNHCT map. The SNHCT combines the average and the P90 NHCT maps; hence, assuring that the selected sweet spots for well placement are statistically the best with regard to the multiple subsurface realizations. The method is applied on the Olympus benchmark case and results are compared to two scenario reduction methods: RMfinder and k-means-k-medoids Clustering. Results show superior performance over the two methods in terms of optimality of the result and the required computational load.

N,S-Heterocycles biodegradation and biosurfactantproduction under CO2/N2 conditions by Pseudomonas and its application on heavy oil recovery

Great challenge of the ultra-heavy oil recovery via cold approaches is attracting the increasing worldwide efforts. Here, we reported a microbial strategy with using the heterocycles-degrading and biosurfactant-producing Pseudomonas strain for heavy oil recovery without thermal energy input. To validate it, we initiated the bacterial evaluation of anaerobically heterocycles degradation and biosurfactant production under CO2 and N2 conditions. Results demonstrated that the CO2 can significantly facilitate the anerobic degradation and influence the yield and diversity of biosurfactant. Structural identification of the biosurfactants by HPLC-MS showed that the di-rhamnolipids and lipopeptides (iturin and fengycin) were respectively accounted for more than 80% and 60% under CO2 condition when fed with carbazole and dibenzothiophene. Degradation of heavy oil under CO2 condition made the better performance on the oil viscosity reducing, the surface tension reducing, emulsification, and oil degrading rate than that under N2 condition. Huff-puff field trials in Jilin oilfield successfully demonstrated that the cumulative oil production was increased from 48 tons to 566 tons with the effective period of more than 9 months, and the viscosity of the produced oil was significantly decreased by 50–65%. These results suggested that this green strategy was promising and economical to the oil recovery for the ultra-heavy reservoir where is easily accessible to CO2.

Influence of grid-orientation effect on modeling of Iraqi oil field: case studies

Reservoir simulation models are utilized by oil and gas companies with a purpose to develop fields. Expansions and improvements in simulation software have lessened the time to develop a model. Simulating the reservoir aims to realize fluid flow, physical, and chemical procedures happening in a hydrocarbon reservoir adequately well for the reason of improving hydrocarbon recovery under various working stipulations. Grid-orientation effects are complicated problem in numerical reservoir simulation. These influences were coming when utilized of numerical utilization mechanism to conditions characterizing physically inconstant displacement procedure. These impacts happen in an assortment simulation of inappropriate and favourable mobility ratio displacements. The current study presents a building static and dynamic model and suggests a development plan for the reservoir under study and change the orientation of the grids to examine the effects of these changes on the values of oil production rate, water cut, pressure, cumulative oil production, and recovery factor. The case studies for an Iraqi oil field which is located in the South East of Iraq. Different values of recovery factors were obtained when the orientation of the grids had been changed.

Application of microbial huff and puff technology in enhancing oil recovery in Weixing oilfield

After a long term of water flooding, the near well area of Weixing oilfield was blocked by a large amount of colloids, asphaltenes and waxes, resulting in a decline in oil well production. It is urgent to explore new technologies to slow the decline of production and increase the production per well. The results of microbial reservoir adaptability evaluation show that the microbial huff and puff test is suitable for Weixing oilfield. 5 pilot test Wells were selected under the well selection principle, and the compatibility of bacillus subtilis SL-2 with native reservoir bacteria and the emulsifying effect on crude oil were evaluated. The corresponding huff and puff scheme is designed according to the production status, pay-zone thickness, treatment radius and porosity. After microbial huff and puff, the daily liquid production increased from 28.2 t/d to 63.4 t/d, the oil production increased from 9.7 t/d to 25.0 t/d. Throughput is valid for 240 days, the cumulative oil production of five wells was2048 t, and the economic benefit was good. After resuming production, the bacterial concentration of the produced liquid rose significantly to more than 107 cfu/mL, indicating that the injected microbial strain grew well in the target reservoir and had good oil-increasing potential.

Study on potential and distribution of remaining oil after polymer flooding in XX Oilfield

XX oilfield has been put into polymer flooding development for 24 years, with cumulative oil production reaching XX million tons. Since 2005, the first-class reservoirs of early polymer flooding have been transferred to subsequent water flooding. After long-term water flooding, polymer flooding and subsequent water flooding, these reservoirs are in the development stage of “double extra high”, with high reservoir production degree, serious ineffective circulation and highly dispersed remaining oil, which makes it difficult to further tap potential. Therefore, the remaining oil potential and distribution law are studied in the first class oil layer in XX area of oilfield, which provides geological basis for further development and adjustment of EOR.

A Novel Streamline-Based Objective Function for Well Placement Optimization in Waterfloods

AbstractIn reservoir development plans, well placement optimization is usually performed to better sweep oil and reduce the amount of trapped oil inside reservoirs. Long-term optimization of well placement requires multiple times simulation of reservoirs which makes these problems cumbersome, especially when a large number of decision variables exist. Cumulative oil production (COP) or net present value (NPV) functions are commonly used as the objective function of optimal enhance oil recovery projects. Use of these functions requires a full-time reservoir simulation and their convergence could be difficult with the chance to be trapped in local optimum solutions. In this study, the novel proportionally distributed streamlines (PDSLs) target function is proposed that can be minimized to reach the optimal well placement. PDSL can be estimated even without full-time reservoir simulation. PDSL tries to direct the appropriate number of streamlines toward the regions with larger amount of oil in the shortest time and hence can improve oil recovery. Particle swarm optimization (PSO) method linked to an in-house streamline-based reservoir simulator is implemented to optimize well placement of water-flooding problems in a two-dimensional heterogeneous reservoir model.

Rock classification in the Eagle Ford Formation through integration of petrophysical, geological, geochemical, and geomechanical characterization

Integration of geomechanical, geological, geochemical, and petrophysical characterization is critical to enhance production from organic-rich mudrocks. This paper introduces an integrated rock classification method in the Eagle Ford Formation shales and marls in southern Texas, consisting of organic-rich fossiliferous marine shale deposited during the Late Cretaceous. A joint inversion of triple-combo, spectral gamma-ray, and elemental capture spectroscopy logs was initially conducted to estimate volumetric concentrations of minerals, porosity, and fluid saturation. Effective elastic properties such as Young's modulus and Poisson's ratio as well as minimum horizontal stress were then estimated as a function of depth. Rock classification was finally performed based on geologic texture and geochemical properties, as well as well log-based estimates of petrophysical and geomechanical properties. The introduced method was applied to two wells located in the oil window of the Eagle Ford Formation (average gas-to-oil ratio of less than 2000 MCF/STB). The results of the integrated rock classification demonstrated similar organic richness and petrophysical properties in both wells. However, the geomechanical rock classification derived from in-situ stress profiles suggested higher proportions (55% vs. 34% of net thickness, respectively) of the better completions quality rock class, with lower minimum horizontal stress (MHS), in one of the wells. A 90-day report on the cumulative oil production of this well shows an additional 11,000 bbl (i.e., 20% more oil production) compared to the second well. This observation was accounted for by geomechanical properties and the distribution of rock classes that differentiate reservoir quality and production.

Auto Gas Lift-The Intelligent Well Solution to Increase Productivity

As the global demand for energy keeps growing, the demand for oil and gas will keep mounting. Billions of dollars are being spent by oil and gas industries to set up the infrastructures of smart field. Managing these fields involves combined methods and knowledge for unmanned and independent handling of these field an enough number of synchronized data is gathered and monitored from wells for smooth and uninterrupted operation. Among all forms of artificial lift, the most common used is automatic gas lift, which is mostly used for offshore wells. The reason for its simplicity and relative use is its downhole flexibility, simplicity, reliability, and capability to operate over a widespread range of flow rates with restricted well head space. Implying the Automatic Gas lift (AGL) technique can cut the operational cost and increases the “Net Present Value” (NPV) by maximizing recovery from a field. In this study, GAP models were being made for DN horizontal, DI Vertical, Non-Contagious (NC), and AGL. The models made were used to predict cumulative oil production, gas production, water production, and pressure drop. After all the predictions were made comparative study was done in order to understand the trends. The results demonstrate substantial increase in oil recovery using Auto Gas Lift in the field through vertical wells 1,2 and 3 which otherwise is quite low in do-nothing case. Moreover, it has been observed that Auto Gas Lift provides higher recovery compared to vertical wells. Hence, fewer number of horizontal wells can be bored in the field to increase revenues and ultimate increase in efficiency of the reserves.