**Source**

**Conference**

- 16th World Petroleum Congress (1)
- European Formation Damage Conference (1)
- SPE Europec/EAGE Annual Conference and Exhibition (1)
- SPE Kuwait International Petroleum Conference and Exhibition (1)
- SPE Kuwait Oil and Gas Show and Conference (1)
- SPE Latin American and Caribbean Petroleum Engineering Conference (1)
- SPE Middle East Oil & Gas Show and Conference (1)
- SPE Middle East Oil and Gas Show and Conference (1)
- SPE Reservoir Simulation Symposium (1)

**Theme**

**Author**

- Abdel Rasoul, Reda Rabiee (2)
- Abdelkhalik, Ossama (1)
- Al-Arouj, Mutlaq (1)
- Al-Enezi, Bashayer (1)
- Al-Enzi, Bashayer (2)
- Amari, Mustafa (2)
- Chebab, Abderahmane (1)
- Dahroug, Ahmed Mahmoud (1)
**Daoud, Ahmed (9)**- Dashti, Qasem (3)
- Dayem, Mahammad Ahmad (1)
- Descubes, Elena (1)
- El-Tayeb, Sayed (1)
- Fava, Guido (1)
- Ghodbane, Fathi (1)
- Goh, Shu Ting (1)
- Lee, W. John (1)
- McVay, Duane Allen (1)
- Minneci, Giovanni (1)
- Ogele, Chile (1)
- Prakash, Roshan (1)
- Salah, Ahmed (1)
- Sharifzadeh, Ahmad (2)
- Sharifzadeh-Najafi, Ahmad (1)
- Tiribelli, Achille (1)
- Torrens, Richard (1)

**Concept Tag**

- Ahnet Basin (1)
- algerian sahara central (1)
- analytical model (1)
- annular space (1)
- Artificial Intelligence (4)
- balance (2)
- Bayesian Inference (1)
- black oil (2)
- cement (1)
- communication (1)
- compartment (1)
- Completion Installation and Operations (1)
- composition (3)
- contribution (2)
- controller (1)
- correlation (1)
- coupling (1)
- current approach (1)
- current modeling approach (1)
- damage (1)
- different fwl (1)
- drilling operation (1)
- dynamic penalty function evolution algorithm (1)
- energy (2)
- enlargment (1)
- EOS model (1)
- equation (2)
- equation of state (1)
- equilibrium (1)
- estimates of resource in place (1)
- evolutionary algorithm (1)
- Existence (1)
- explanation (1)
- exploration well (1)
- fluid modeling (1)
- formation (2)
- formation evaluation (1)
- Formation Isolation Valve (1)
- gas Condensate (1)
- gas production (1)
- heat (2)
- history matching (1)
- hole (1)
- Horizontal (1)
- horizontal well (1)
- hydrocarbon (2)
- IAM approach (1)
- information (1)
- initialization (1)
- knowledge management (1)
- layer (2)
- likelihood (1)
- likelihood distribution (1)
- machine learning (2)
- Material Balance (1)
- material balance analysis (1)
- Mmstb (1)
- model (2)
- modeling (2)
- Modeling & Simulation (2)
- modeling approach (1)
- network model (1)
- node (2)
- north kuwait jurassic (1)
- OHIP (1)
- oil and gas (1)
- operator (1)
- overlap (1)
- paper (1)
- Paper SPE (1)
- perforation (2)
- permit (1)
- physical explanation (1)
- plt interpretation (2)
- pore (1)
- posterior (1)
- posterior distribution (1)
- pressure data (2)
- pressure higher (1)
- probability (1)
- probability distribution (1)
- problem (1)
- production (1)
- production control (2)
- production logging (2)
- production monitoring (2)
- profile (2)
- PVT measurement (1)
- PVT Sample (1)
- Reservoir Characterization (1)
- reservoir description and dynamics (3)
- reservoir simulation (4)
- Reservoir Surveillance (2)
- SA field (1)
- Scenario (2)
- SPE (3)
- Upstream Oil & Gas (9)
- water (2)
- well (2)
- wellbore (2)

**File Type**

Fava, Guido (Schlumberger) | Descubes, Elena (Schlumberger) | Daoud, Ahmed (Schlumberger) | Sharifzadeh-Najafi, Ahmad (Schlumberger) | Al-Enezi, Bashayer (Kuwait Oil Company) | Al-Arouj, Mutlaq (Kuwait Oil Company) | Dashti, Qasem (Kuwait Oil Company)

Sabriyah and Raudhatain are the main fields producing from the Middle Marrat Jurassic formation in North Kuwait with approximately 5 km distance between the two fields. Raudhatain fluid is considered as Volatile Oil, while Sabriyah is described as Gas-condensate. 16 PVT samples from Raudhatain were analyzed and described as Volatile oil. 12 PVT samples taken from Sabriyah field where 7 samples show gas condensate behavior and rest shows volatile oil.

A key challenge in understanding the Sabriyah fluid characterization is the fact that 5 well samples that showed Volatile oil behavior are not separated from the Gas condensate wells by any apparent barrier. In addition, the initial reservoir pressure is much higher than the saturation pressure, preventing the equilibrium of those fluids.

The objectives for this study are to analyze the physical explanation of coexistent of oil and gas-Condensate in one communicated reservoir with reservoir pressure higher than saturation pressure, apply different modeling approaches to accurately describe the fluid behavior in Sabriyah field and finally capture the influence of uncertainty in the type of fluid on the production forecast.

The physical explanation for this phenomenon was investigated from different points of view: the variation of temperature, compositional variation with depth, existence of geological barriers, and facies changes. It was found that the compositional variation with depth and the change of fluids with changes of facies can provide reasonable explanation for this phenomenon. The first explanation related to compositional variations with depth is supported by the observed data that shows a strong relationship between depth and fluid type, while the temperature did not influence significantly the gas-oil phase change. The second explanation related to the concept of gas and oil charge depending on facies is supported by mercury injection capillary pressure data taken from different depth in the reservoirs, this concept improves the understanding of fluid distribution which could not be explained in previous approaches.

This paper shows the way of modeling this phenomena based on these two explanations, which honor both static and dynamic data with special reference to the effect of these different modeling approaches on the production forecast of Sabriyah field.

The near critical fluids which are the type of fluids in Sabriyah field are usually problematic to handle with Equation of State; therefore solving this particular case is expected to add technical value to reservoirs of the same type of fluids.

The facies dependence of gas and oil distribution and the way of modeling this phenomenon is an innovative view that can contribute to the description of similar fields.

SPE Disciplines: Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)

North Kuwait Jurassic Complex consists of 6 fields with 4 major reservoirs. Total of 19 integrated reservoir models incorporating seismic, geological, petrophysical, and engineering data have been constructed to delineate the reservoirs and perform multi-scenario production forecast for the whole field. These models are dual porosity, with near critical fluids of gas condensate and volatile oil. There are different challenges to model the whole fields that can be summarized as follows. First, the two main producing reservoirs among all fields are (RA) and (SA) which account for about

This paper explains the approach adopted to solve most of these challenges. First, a numerical approach was implemented to allow communication between these blocks of different FWL's at the hydrocarbon zone only. Thus, ensure the equilibration of the system with honoring the geological understanding and allowing pressure communication through the hydrocarbon leg. Second, a proposed approach of using the standalone simulation models for running different sensitivities to select the optimum development plan per each field and then couple them with one controller at the surface to get the optimum field development plan for the whole fields as one asset producing through one common surface facility. Third a black oil delumping approach was used to convert the black oil to 35 pseudo components based on different EOS models generated per each field. Thus getting the advantage of speeding up the run based on using black oil simulation and reporting the change in composition with time using a simple black oil delumping look up table.

Torrens, Richard (Schlumberger) | Daoud, Ahmed (Schlumberger) | Amari, Mustafa (Schlumberger) | Sharifzadeh, Ahmad (Schlumberger) | Prakash, Roshan (Schlumberger) | Al-Enzi, Bashayer (Kuwait Oil Company) | Dashti, Qasem (Kuwait Oil Company)

A project was undertaken to construct an overview to build an integrated asset model (IAM) of an onshore fractured carbonate gas condensate and volatile oil asset in Northern Kuwait that is considered the first gas asset discovered in Kuwait. The asset has the potential to produce from six distributed fields producing from four hydrocarbon-bearing structures. The development strategy calls for extensive drilling and facilities expansion to increase and sustain production with the potential addition of depletion compression to further sustain the plateau. Because the reservoirs are highly compartmentalized, they are split into 19 separate models. Production is through three surface facilities, fluids vary significantly across the field from sour gas condensate to volatile oil, and it is important to consider the impact of reservoir deliverability, facilities capacity, and surface backpressure when evaluating different development scenarios.

A novel IAM was constructed that integrates reservoirs, wells, pipelines, and facilities models into an integration platform. The IAM comprises 19 black oil dual porosity reservoir models coupled to a compositional network model via black oil delumping to convert the subsurface rates into six-components composition. A split table (compositional delumping) is then used to convert the six-components composition to 35 surface components to be used in the equation-of-state (EOS) surface network models to estimate the composition at each point at the surface (inlet and outlet of each facility). Then the network model is coupled to surface facilities modeling to estimate the rates and composition at the export level. This idea of mapping the subsurface fluid from black oil at subsurface to compositional at surface reduces the subsurface running time and makes the IAM more feasible from the running time perspective.

The IAM has highlighted several differences versus the stand-alone modeling and the coupled modeling at the surface only. First, more accurate accounting for backpressure results in an increase in the plateau. Second, a production forecast for each facility gives a detailed analysis of production and the number of wells for each facility. Finally, detailed compositional information becomes available at all points in the surface network, which is important input to the facilities design.

doi: 10.2118/MS

SPE-173308-MS

Artificial Intelligence, black oil, composition, current approach, current modeling approach, EOS model, equation of state, fluid modeling, IAM approach, information, Modeling & Simulation, network model, reservoir simulation, Scenario, simulation model, subsurface, surface network, Upstream Oil & Gas, well stream

SPE Disciplines:

Technology:

This paper presents a novel implementation for evolutionary algorithms in oil and gas reservoirs history matching problems. The reservoir history is divided into time segments. In each time segment, a penalty function is constructed that quantifies the mismatch between the measurements and the simulated measurements, using only the measurements available up to the current time segment. An evolutionary optimization algorithm is used, in each time segment, to search for the optimal reservoir permeability and porosity parameters. The penalty function varies between segments; yet the optimal reservoir characterization is common among all the constructed penalty functions. A population of the reservoir characterizations evolves among subsequent time segments through minimizing different penalty functions. The advantage of this implementation is two fold. First, the computational cost of the history matching process is significantly reduced. Second, problem constraints can be included in the penalty function to produce more realistic solutions. The proposed concept of dynamic penalty function is applicable to any evolutionary algorithm. In this paper, the implementation is carried out using genetic algorithms. Two case studies are presented in this paper: a synthetic case study and the PUNQ-S3 field case study. A computational cost analysis that demonstrates the computational advantage of the proposed method is presented.

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Management and Information > Information Management and Systems > Artificial intelligence (1.00)
- Management and Information > Professionalism, Training, and Education > Communities of practice (0.93)
- Management and Information > Information Management and Systems > Knowledge management (0.93)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)

We present a methodology of allocating gas rate and associated water to each individual layer using temperature measurements and total surface production of gas and water. This paper consists of two parts. In part one; we propose an analytical forward model for wellbore temperature response under two-phase production in a multilayer geometry, using a nodal representation of the well. This model accounts for the formation geothermal gradient, steady-state gas-water flow in the wellbore, friction loss and Joule-Thomson effect in the wellbore, contrast in the thermal and physical properties of gas and water, wellbore heat losses due to unsteady heat conduction in the earth, and the mixing of the fluid streams of contrasting temperature. The second part shows the solution technique used to allocate the gas and water rate for each layer using the genetic algorithm and the constructed software by knowing the temperature measurements inside the wellbore and by using the previously derived forward model for temperature response along with commercial software packages used to estimate the pressure loss required by the temperature forward model.

Two synthetic cases are used to test the validity of the new developed forward model; the first one is account for a well that produces from a single layer, while the second one produces from multilayer well in which the temperature in the wellbore and production rate is known. The developed model is applied to calculate the temperature profile inside the wellbore. The calculated profile is compared with the actual profile. The results showed that the new developed model is valid and reliable.

The practical implementation of the new developed production allocation model is examined on data from two actual gas wells with temperature measurements taken from Production logging tools recorded in these wells. The results showed that the model succeeded to accurately allocate the flow rate of gas and water phase for the multilayer producing wells based only on the temperature measurements inside the well bore and the total surface rates.

**Genetic Algorithm**

The optimization process proposed in this work is based on genetic algorithm coupled to the developed analytical model. Genetic algorithm is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.Table 1 shows the GA parameters used in this work.This algorithm searches for the flowing gas rate in each layer in the multilayer gas wells in case of single gas flow and for the flowing gas and water rate in each layer in case of multiphase flow (i.e. gas and water) respecting the constraints imposed which are the maximum and minimum flowing rate in each layer .The constraints are defined as input variables to the genetic algorithm that explores the best combinations that minimize the objective function. The arrangement of all optimization variables along with the developed analytical model will provide an individual in a population. Each individual is evaluated based on the objective function values.

analytical model, Artificial Intelligence, balance, contribution, energy, equation, formation evaluation, heat, layer, model, node, perforation, plt interpretation, production control, production logging, production monitoring, profile, reservoir description and dynamics, Reservoir Surveillance, SPE, Upstream Oil & Gas, water, well, wellbore

SPE Disciplines:

We present a methodology of allocating gas rate and associated water to each individual layer using temperature measurements and total surface production of gas and water. This paper consists of two parts. In part one; we propose an analytical forward model for wellbore temperature response under two-phase production in a multilayer geometry, using a nodal representation of the well. This model accounts for the formation geothermal gradient, steady-state gas-water flow in the wellbore, friction loss and Joule-Thomson effect in the wellbore, contrast in the thermal and physical properties of gas and water, wellbore heat losses due to unsteady heat conduction in the earth, and the mixing of the fluid streams of contrasting temperature. The second part shows the solution technique used to allocate the gas and water rate for each layer by knowing the temperature measurements inside the wellbore and by using the previously derived forward model for temperature response along with commercial software packages used to estimate the pressure loss required by the temperature forward model.

Two synthetic cases are used to test the validity of the new developed forward model; the first one is account for a well that produces from a single layer, while the second one produces from multilayer well in which the temperature in the wellbore and production rate is known. The developed model is applied to calculate the temperature profile inside the wellbore. The calculated profile is compared with the actual profile. The results showed that the new developed model is valid and reliable.

The practical implementation of the new developed production allocation model is examined on data from two actual gas wells with temperature measurements taken from Production logging tools recorded in these wells. The results showed that the model succeeded to accurately allocate the flow rate of gas and water phase for the multilayer producing wells based only on the temperature measurements inside the well bore and the total surface rates.

SPE Disciplines:

The transition from completion to production often requires the well to be killed immediately after perforation is completed, thus exposing the formation to potentially damaging kill fluid. To obtain a perforation tunnel with maximum productivity, this transition requires an optimal cleanup and the removal of the perforation damages.

A new underbalanced oriented perforating technique has been successfully implemented in Algeria. It combines the use of a formation isolation valve (FIV) to keep damaging completion fluid off the formation immediately after perforation and a perforating technique that utilizes the dynamic underbalanced method, which cleans perforations with more efficiency than conventional static underbalanced perforating method. In addition, a passive gun-orienting system was used to optimize the perforating process and enhance the well's performance.

The new technique was applied in 2003 to horizontal Well-1, which was drilled by in the Tadrart sandstone formation of the Berkine basin. After successful results in this well, the operator adopted its use in 2005 for two additional wells, Well-2 and Well-3.

The paper describes the application of the new technique to three horizontal wells of the Berkine basin and the evaluation of the related productivity increase vs. the conventional perforating method.

Oilfield Places:

- Africa > Middle East > Libya > NC169A Concession > Tadrart Formation (0.99)
- Africa > Middle East > Algeria > Eastern Algeria > Berkine Basin (0.99)
- North America > United States > Texas > Gulf Coast Basin > Weber Field (0.98)

**ABSTRACT**

Estimating original hydrocarbons in place (OHIP) in a reservoir is fundamentally important in estimating reserves and potential profitability. Two traditional methods for estimating OHIP are volumetric and material balance methods. Probabilistic estimates of OHIP are commonly generated prior to significant production from a reservoir by combining volumetric analysis with Monte Carlo methods. Material balance is routinely used to analyze reservoir performance and estimate OHIP. Although material balance has uncertainties due to errors in pressure and other parameters, probabilistic estimates are seldom generated.

In this paper we use a Bayesian formulation to integrate volumetric and material balance analyses and to quantify uncertainty in the combined OHIP estimates. Specifically, we apply Bayes' rule to the Havlena and Odeh material balance equation to estimate original oil in place, *N,* and relative gas-cap size, *m,* for a gas-cap drive oil reservoir. We consider uncertainty and correlation in the volumetric estimates of *N* and *m* (reflected in the prior probability distribution), as well as uncertainty in the pressure data (reflected in the likelihood distribution). Approximation of the covariance of the posterior distribution allows quantification of uncertainty in the estimates of *N* and *m* resulting from the combined volumetric and material balance analyses.

Our investigations show that material balance data reduce the uncertainty in the volumetric estimate, and the volumetric data reduce the considerable non-uniqueness of the material balance solution, resulting in more accurate OHIP estimates than from the separate analyses. One of the advantages over reservoir simulation is that, with the smaller number of parameters in this approach, we can easily sample the entire posterior distribution, resulting in more complete quantification of uncertainty.

**INTRODUCTION**

The estimation of original hydrocarbons in place (OHIP) in a reservoir is one of the oldest and, still, most important problems in reservoir engineering. Estimating OHIP in a reservoir is fundamentally important in estimating reserves and potential profitability. We have long known that our estimates of OHIP possess uncertainty due to scarcity of data and data inaccuracies.[1-3] Quantifying the uncertainties in OHIP estimates can improve reservoir development and investment decision-making for individual reservoirs and can lead to improved portfolio performance.[4] The general question we address in this paper is: Given reservoir data available, how do we best estimate OHIP and how do we quantify the uncertainty inherent in this estimate?

Two traditional methods for estimating OHIP are volumetric and material balance methods.[5,6] Volumetric methods are based on static reservoir properties, such as porosity, net thickness and initial saturation distributions. Since they can be applied prior to production from the reservoir, volumetric methods are often the only source of OHIP values available in making the large investment decisions required early in the life of a reservoir. Given the often large uncertainty due to paucity of well data early in the reservoir life, it is common to quantify the uncertainty of volumetric estimates of OHIP using statistical methods such as Confidence Interval[7] and Monte Carlo analysis.[8,9]

Material balance is routinely used to analyze reservoir performance data and estimate OHIP. The material balance method requires pressure and production data and, thus, can be applied only after the reservoir has produced for a significant period of time. The advantages of material balance methods are (1) we can determine drive mechanism in addition to OHIP, (2) no geological model is required, and (3) we can solve for OHIP (and sometimes other parameters) directly from performance data. Primary sources of uncertainty in material balance analyses are incomplete or inaccurate production data and inaccuracies in determining an accurate average pressure trend, particularly in low-permeability or heterogeneous reservoirs. Although these uncertainties have been long recognized, material balance methods are often considered more accurate than volumetric methods, since they are based on observed performance data. It is not common practice to formally quantify the uncertainty in material balance estimates of OHIP, although there have been some attempts.[10-13]

Artificial Intelligence, Bayesian Inference, correlation, estimates of resource in place, likelihood, likelihood distribution, machine learning, Material Balance, material balance analysis, Mmstb, OHIP, overlap, posterior, posterior distribution, pressure data, probability, probability distribution, quantify, reserves evaluation, reservoir simulation, resource in place estimate, Upstream Oil & Gas, volumetric analysis

SPE Disciplines:

PROBLEMS OF CEMENTING EXPLORATION WELLS IN ALGERIAN SAHARA " PROBLEMS OF CEMENTING EXPLORATION WELLS IN ALGERIAN SAHARA CENTRAL - AHNET BASIN (IN-SALAH AREA) " Abderahmane Chebab, Sonatrach Exploration Division, Algeria Ahmed Daoud, Sonatrach Algeria THE PROBLEM 5/8"x 13 3/8" and has filled all the rig hole enlargment. A lot of wells located in the IN-SALAH Programm of cementing repair has been area ( the Ahnet Basin) often present gas traces realized with an acidification. Another coming of when the cementing column 13 3/8" has been gas has taken place during the penetration of the achieved. Ordovician reservoir and it has been necessary to The gas leaks appear along the space close BOP on drill pipe and the annular pressure of annular between the casing and the cement. the casing 9 5/8"x 13 3/8" is climbed until 1500PSI. This leads to a pressure increase between different casing and the gas is dropping at times at 2. Second case:Well DTS-1 Block 340 the level of the hole enlargment of the rig ( for Permit In Bazzene example wells ISS-1,GBF-2,MSR-1,DTS-1 and IS- After the run in hole of the casing 13 3). 3/8"and its cementing, bubbles of gas have The faillures of cementing 13 3/8" is appeared inside the casing and in the annular space generally accompanied by gas occurrences through of casing 18 5/8"x13 3/8" and around the casing 18 the floor beneath the rig or increasing pressure 5/8". between the casing and the annular space. Attempts of squeeze of cement behind the The consequence gas migration causes casing 13 3/8" have not given good result. interzone communication wich will have a real effect in production. 3. Third case:well GBF-2 Block 343 In some cases the well will be abandoned Permit Tegentour for safety reasons particulary when gas occurs. After the run in hole of the casing 13 3/8" The operation of the cementing repair is and its cementing, an important gas arrival through difficult and costly. the annular space has been observed because of the The sedimentary column encountered bad cementing .A cementing repair by the shoe during drilling by the five exploration wells choose casing has been achieved but without result.An for this presentation is constituted mainly by clay other cementing repair by perforating of squeeze sandstone and limestone. has been realized behind the casing 13 3/8" with un The main reservoirs are localized to the improved cement and has allowed to stop definitely level of sandstone of carboniferous, upper and the arrival of the gas. lower devonian and cambro-ordovician age. 4. Fourth case:Well ISS-1 Block 342 Pockets of gas that are the cause of Permit Tegentour channeling gas problems in the annular space and After the run in hole of the casing 13 3/8" behind the differents casing and deteriorates the and its cementing it have had an gas arrival through quality of the cementing are buried in clays of the the annular space of the casing 13 3/8"x18 5/8". An Visean, Tournaisian, Devonian and Silurian. operati

Geologic Time:

- Phanerozoic > Paleozoic > Ordovician (1.00)
- Phanerozoic > Paleozoic > Devonian > Lower Devonian (0.36)

Oilfield Places: Africa > Middle East > Algeria > Central Algeria > Ahnet-Timimoun Basin > In Salah Field (0.98)

SPE Disciplines:

Thank you!